Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

CODEX SKILL THAT FINDS WASTED STORAGE ON YOUR MAC! I made a Codex skill that audits your Mac and reports where disk space can be cleaned up safely. Scan your Mac while Codex checks caches, logs, old installers, build artifacts, node_modules, Xcode data, Docker leftovers, and other files that...

Kappaemme1926's profile picture

Kappaemme

4,344 subscribers

33,032 Aufrufe • vor 3 Monaten •via X (Twitter)

Wissenschaft & TechnologieBildungGesundheit & Wellness
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

CODEX SKILL THAT STOPS WASTING YOUR CODEX USAGE! I made a Codex skill that gives every task a clear budget before it starts. Choose Saver, Balanced, Full Power, or define your own limits. It reduces: → unnecessary repository scans → repeated commands and huge logs → speculative refactors → redundant web research → unnecessary subagents → endless retry loops It still runs the essential checks needed for a trustworthy result. If the budget isn’t enough, it creates a Markdown checkpoint you can open in Codex and resume later. It doesn’t increase your account limits. It helps you use them better. Install: npx --yes codex-budget-mode@latest 100% open source. Repo in bio.
0:10

CODEX SKILL THAT STOPS WASTING YOUR CODEX USAGE! I made a Codex skill that gives every task a clear budget before it starts. Choose Saver, Balanced, Full Power, or define your own limits. It reduces: → unnecessary repository scans → repeated commands and huge logs → speculative refactors → redundant web research → unnecessary subagents → endless retry loops It still runs the essential checks needed for a trustworthy result. If the budget isn’t enough, it creates a Markdown checkpoint you can open in Codex and resume later. It doesn’t increase your account limits. It helps you use them better. Install: npx --yes codex-budget-mode@latest 100% open source. Repo in bio.

Kappaemme

12,157 Aufrufe • vor 1 Monat

Introducing BlitzClean ! A free, open-source RAM monitor + disk cleaner for Mac I built it based on 3 things: - I kept running out of storage - I never knew what was eating my RAM - Memory Clean 2 stopped working for me on the macOS 27 beta (Golden Gate) What it does: - CPU, RAM and free disk in the menu bar - Ranks apps + AI agents (Claude Code, Codex, Cursor) by RAM, so you can pause or quit the ones you forgot about - Finds what fills your disk - Cleans caches, node_modules, simulators, Docker and old worktrees (you review everything first) MIT, runs 100% locally Made the video with Opus 5.5 medium, what do you think ?
0:15

Introducing BlitzClean ! A free, open-source RAM monitor + disk cleaner for Mac I built it based on 3 things: - I kept running out of storage - I never knew what was eating my RAM - Memory Clean 2 stopped working for me on the macOS 27 beta (Golden Gate) What it does: - CPU, RAM and free disk in the menu bar - Ranks apps + AI agents (Claude Code, Codex, Cursor) by RAM, so you can pause or quit the ones you forgot about - Finds what fills your disk - Cleans caches, node_modules, simulators, Docker and old worktrees (you review everything first) MIT, runs 100% locally Made the video with Opus 5.5 medium, what do you think ?

Virgile RIETSCH

155,726 Aufrufe • vor 18 Tagen

This app uses AirDrop to send files from your Android phone to your Macbook! Yes, it actually uses AirDrop. That means you don't have to install ANYTHING on your Mac to send files from your Android phone! Here's a video of a Galaxy Z Flip 5 AirDropping a file to a Macbook running macOS Ventura 13.5.1. (Thanks to u/FragmentedChicken for testing this app for me and sharing the video!) A few months ago, Twitter user @Linus13499209 brought an app called WarpShare to my attention. WarpShare is an app made by the developers of MoKee, an AOSP-based custom ROM that was popular in China. Since MoKee wasn't as popular outside of China, it seems the existence of their WarpShare app slipped under the radar. I was skeptical about whether it would work at all. Grishka, the developer of NearDrop, an open source port of Google's Nearby Share to macOS, told me that they were under the assumption that AirDrop requires the use of AWDL (Apple Wireless Direct Link, Apple's proprietary WiFi-based protocol) to communicate both ways. However, it seems that AWDL is only required for your Android phone to be discoverable by your Mac (ie. to send files from your Mac to your Android phone) but not the other way around. Because of this, though, WarpShare only supports sending files from Android to Mac but not vice versa. Your Mac also needs to have AirDrop discoverability set to "everyone" for this to work, as "contacts-only" requires Apple-signed certificates. Plus, it also doesn't support sending files from Android to iPhones or iPads, even when "everyone" mode is enabled. Still, if you find other Android --> Mac file sharing options to be lackluster, give WarpShare a try! The fact that it works at all is incredible, which is why I'm sharing this news here. If you want to download WarpShare on your Android device, you'll need to compile the app from its source code. If you're a Patron/X subscriber, however, I will share my compiled APK with you. WarpShare source code:
0:16

This app uses AirDrop to send files from your Android phone to your Macbook! Yes, it actually uses AirDrop. That means you don't have to install ANYTHING on your Mac to send files from your Android phone! Here's a video of a Galaxy Z Flip 5 AirDropping a file to a Macbook running macOS Ventura 13.5.1. (Thanks to u/FragmentedChicken for testing this app for me and sharing the video!) A few months ago, Twitter user @Linus13499209 brought an app called WarpShare to my attention. WarpShare is an app made by the developers of MoKee, an AOSP-based custom ROM that was popular in China. Since MoKee wasn't as popular outside of China, it seems the existence of their WarpShare app slipped under the radar. I was skeptical about whether it would work at all. Grishka, the developer of NearDrop, an open source port of Google's Nearby Share to macOS, told me that they were under the assumption that AirDrop requires the use of AWDL (Apple Wireless Direct Link, Apple's proprietary WiFi-based protocol) to communicate both ways. However, it seems that AWDL is only required for your Android phone to be discoverable by your Mac (ie. to send files from your Mac to your Android phone) but not the other way around. Because of this, though, WarpShare only supports sending files from Android to Mac but not vice versa. Your Mac also needs to have AirDrop discoverability set to "everyone" for this to work, as "contacts-only" requires Apple-signed certificates. Plus, it also doesn't support sending files from Android to iPhones or iPads, even when "everyone" mode is enabled. Still, if you find other Android --> Mac file sharing options to be lackluster, give WarpShare a try! The fact that it works at all is incredible, which is why I'm sharing this news here. If you want to download WarpShare on your Android device, you'll need to compile the app from its source code. If you're a Patron/X subscriber, however, I will share my compiled APK with you. WarpShare source code:

Mishaal Rahman

1,290,866 Aufrufe • vor 3 Jahren

I just built a Claude skill that audits your entire Google Ads account in under 5 minutes 🤯 One prompt → a full account score, wasted spend breakdown, and a prioritized fix list telling you exactly what to change this week. All inside Claude Cowork. Perfect for DTC brands and agencies who are running Google Ads but have no idea how much budget is leaking. If you're managing Google Ads and your "optimization" process is logging in, staring at the dashboard, sorting by cost, and hoping you spot the problem before it costs you another $500... This audit skill finds it for you: → Connects to your live Google Ads data via MCP → Scores your account across 6 dimensions: wasted spend, search term quality, keyword health, quality scores, budget allocation, and creative performance → Calculates your exact wasted spend in dollars — search terms burning budget with zero conversions → Flags quality score issues dragging up your CPCs → Identifies keyword cannibalization across campaigns → Surfaces your top 5 highest-priority fixes ranked by budget impact → Generates a clean audit report you can hand to a client or share with your team No CSV exports. No pivot tables. No guessing where the money went. What you get: → A single Claude skill file you install once → An account health score (0-100) every time you run it → Exact dollar amount of wasted spend identified → Prioritized action list — not "optimize your account," but "pause these 12 search terms and save $847/month" → Works with any Google Ads account connected I'm giving away the full audit skill — the actual .md file you drop into Claude and run against your own account. Want it? Like this post Comment "SKILL" And I'll send it over (must be following so I can DM)
0:16

I just built a Claude skill that audits your entire Google Ads account in under 5 minutes 🤯 One prompt → a full account score, wasted spend breakdown, and a prioritized fix list telling you exactly what to change this week. All inside Claude Cowork. Perfect for DTC brands and agencies who are running Google Ads but have no idea how much budget is leaking. If you're managing Google Ads and your "optimization" process is logging in, staring at the dashboard, sorting by cost, and hoping you spot the problem before it costs you another $500... This audit skill finds it for you: → Connects to your live Google Ads data via MCP → Scores your account across 6 dimensions: wasted spend, search term quality, keyword health, quality scores, budget allocation, and creative performance → Calculates your exact wasted spend in dollars — search terms burning budget with zero conversions → Flags quality score issues dragging up your CPCs → Identifies keyword cannibalization across campaigns → Surfaces your top 5 highest-priority fixes ranked by budget impact → Generates a clean audit report you can hand to a client or share with your team No CSV exports. No pivot tables. No guessing where the money went. What you get: → A single Claude skill file you install once → An account health score (0-100) every time you run it → Exact dollar amount of wasted spend identified → Prioritized action list — not "optimize your account," but "pause these 12 search terms and save $847/month" → Works with any Google Ads account connected I'm giving away the full audit skill — the actual .md file you drop into Claude and run against your own account. Want it? Like this post Comment "SKILL" And I'll send it over (must be following so I can DM)

Mike Futia

60,509 Aufrufe • vor 6 Monaten

CCleaner used to be the default way to keep a Windows machine fast. Then it got bought, stuffed with ads and bundled crapware, and in 2017 one release was compromised and shipped malware to over 2 million people. Millions still install it every month because nothing else has really replaced it. So, a team called AdventDev just built a free, open-source replacement. It's called Kudu. Open the app and you get one dashboard for the whole machine. > System cleaner for temp files, logs, caches, and crash dumps. > Browser cleaner across every major browser. > Gaming cleaner for launcher and shader caches. > Registry cleaner for broken and orphaned entries. > Startup manager that shows the actual boot impact of each program. > Disk analyzer with an interactive treemap, so you can see where 40GB quietly disappeared to. > Debloater that rips Windows bloatware out. > Program uninstaller that also removes the leftovers other uninstallers leave behind. Then it does the parts CCleaner never did. A malware scanner with signature matching, heuristic analysis, and Windows Defender integration. A privacy shield that lets you flip 30+ Windows privacy settings from one screen: telemetry, ad ID, Cortana, tracking. Secure delete that overwrites files with random data before removing them, so nothing can recover them later. There's a real-time performance monitor with per-core CPU, memory, disk, network, and S.M.A.R.T. drive health. A one-click restore point before every clean, so you can roll back. Scheduled scans. A CLI mode for people who don't want a GUI. Support for 30 languages. Native app for Windows, macOS, and Linux. Intel and Apple Silicon builds. No ads, no upsells, no telemetry, no account. The cleaning rules are plain JSON files, so if your favorite app isn't covered yet, you can add support for it without writing a line of code. MIT license. 100% Open Source. Repo:
0:14

CCleaner used to be the default way to keep a Windows machine fast. Then it got bought, stuffed with ads and bundled crapware, and in 2017 one release was compromised and shipped malware to over 2 million people. Millions still install it every month because nothing else has really replaced it. So, a team called AdventDev just built a free, open-source replacement. It's called Kudu. Open the app and you get one dashboard for the whole machine. > System cleaner for temp files, logs, caches, and crash dumps. > Browser cleaner across every major browser. > Gaming cleaner for launcher and shader caches. > Registry cleaner for broken and orphaned entries. > Startup manager that shows the actual boot impact of each program. > Disk analyzer with an interactive treemap, so you can see where 40GB quietly disappeared to. > Debloater that rips Windows bloatware out. > Program uninstaller that also removes the leftovers other uninstallers leave behind. Then it does the parts CCleaner never did. A malware scanner with signature matching, heuristic analysis, and Windows Defender integration. A privacy shield that lets you flip 30+ Windows privacy settings from one screen: telemetry, ad ID, Cortana, tracking. Secure delete that overwrites files with random data before removing them, so nothing can recover them later. There's a real-time performance monitor with per-core CPU, memory, disk, network, and S.M.A.R.T. drive health. A one-click restore point before every clean, so you can roll back. Scheduled scans. A CLI mode for people who don't want a GUI. Support for 30 languages. Native app for Windows, macOS, and Linux. Intel and Apple Silicon builds. No ads, no upsells, no telemetry, no account. The cleaning rules are plain JSON files, so if your favorite app isn't covered yet, you can add support for it without writing a line of code. MIT license. 100% Open Source. Repo:

Brady Long

150,829 Aufrufe • vor 3 Monaten

I built a custom TradingView indicator with Claude Code & Fable 5. It's called the Storm Gauge and is built off a real quant trading strategy. I open-sourced the full code on GitHub. Free to install, free to fork, yours to improve. Here's how to install a quant indicator on your TradingView chart: What it actually is The Storm Gauge is a live implementation of the GARCH model, a Nobel Prize-winning volatility framework that real quant desks run daily. It forecasts how "violent" tomorrow's market could be by combining three inputs: an asset's baseline volatility, yesterday's shock, and where volatility was already sitting before that shock happened. It doesn't predict market direction. Instead, it measures risk, in real time, on your actual chart. How to install it Method 1. Plugin command Open the GitHub repo: Find the installation section, copy the command, and paste it into Claude Code. It runs the plugin install automatically. Method 2. Manual config Open garchmethod.md in the repo, copy the entire file, and paste it into Claude Code. It fetches the skill files directly and verifies the strategy for you. (you only need one method; I'm just showing both) Getting it onto your TradingView chart Inside the repo, there's a Pine Script folder. Open it, copy the entire file. Go into TradingView's Pine Editor, paste it in, hit Enter, and refresh. That's it. The Storm Gauge now runs live on your chart as a real number. Once it's installed, just talk to it: → "What's the volatility forecast on Bitcoin?" → "Explain what the current volatility forecast means on $BTC and how it should impact my position sizing" → "Help me size my S&P500 position according to current market volatility" Does it actually work? I backtested the same EMA cross strategy two ways across 15 years of BTC data. Same entries, same exits. → Fixed position sizing: $17,957 final equity → Storm Gauge (GARCH) sizing: $21,205 final equity Fewer drawdowns, less risk, better result. Full breakdown of the entire build process in my recent article - pinned on my profile.
0:18

I built a custom TradingView indicator with Claude Code & Fable 5. It's called the Storm Gauge and is built off a real quant trading strategy. I open-sourced the full code on GitHub. Free to install, free to fork, yours to improve. Here's how to install a quant indicator on your TradingView chart: What it actually is The Storm Gauge is a live implementation of the GARCH model, a Nobel Prize-winning volatility framework that real quant desks run daily. It forecasts how "violent" tomorrow's market could be by combining three inputs: an asset's baseline volatility, yesterday's shock, and where volatility was already sitting before that shock happened. It doesn't predict market direction. Instead, it measures risk, in real time, on your actual chart. How to install it Method 1. Plugin command Open the GitHub repo: Find the installation section, copy the command, and paste it into Claude Code. It runs the plugin install automatically. Method 2. Manual config Open garchmethod.md in the repo, copy the entire file, and paste it into Claude Code. It fetches the skill files directly and verifies the strategy for you. (you only need one method; I'm just showing both) Getting it onto your TradingView chart Inside the repo, there's a Pine Script folder. Open it, copy the entire file. Go into TradingView's Pine Editor, paste it in, hit Enter, and refresh. That's it. The Storm Gauge now runs live on your chart as a real number. Once it's installed, just talk to it: → "What's the volatility forecast on Bitcoin?" → "Explain what the current volatility forecast means on $BTC and how it should impact my position sizing" → "Help me size my S&P500 position according to current market volatility" Does it actually work? I backtested the same EMA cross strategy two ways across 15 years of BTC data. Same entries, same exits. → Fixed position sizing: $17,957 final equity → Storm Gauge (GARCH) sizing: $21,205 final equity Fewer drawdowns, less risk, better result. Full breakdown of the entire build process in my recent article - pinned on my profile.

Miles Deutscher

56,625 Aufrufe • vor 3 Monaten

I went a little overboard with Codex last week and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.
0:16

I went a little overboard with Codex last week and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.

雪踏乌云

23,107 Aufrufe • vor 3 Monaten

Goldman pays $27,000 per seat for a Bloomberg Terminal. I found 10 open source tools on GitHub that replicate almost all of it for free. Retail investors have never had this much firepower. Bookmark & Repost this one: 1. OpenBB Stocks, options, crypto, forex, and macro data in one research platform. Build your own dashboards, reports, and AI analysts on top of it. The OG of open source finance. 50K+ stars. 2. FinceptTerminal A full financial terminal: global market data, advanced charts, economic indicators, portfolio analysis, and AI research tools. Windows, Mac, and Linux. 3. Neuberg 516 drag-and-drop panels covering equities, bonds, commodities, currencies, credit, and macro. Even connects to Alpaca, Hyperliquid, and Polymarket so you can trade from the terminal itself. 4. Qlib (by Microsoft) An open source AI platform for quant investing. Train ML models, discover signals, backtest strategies, and build portfolios with the same workflow a quant desk uses. 5. FinRobot An AI equity research team on your laptop. Its agents read financial statements, build DCF valuations, debate bull vs bear cases, and generate full investment reports. 6. EdgarTools Turns the SEC database into something humans can actually use. Pull 10-Ks, 10-Qs, insider trades, executive pay, and hedge fund holdings going back to 1994. 7. LEAN (by QuantConnect) An institutional-grade engine for trading algorithms. Write strategies in Python or C#, backtest on decades of data, then connect to real brokers and go live. 8. FinanceToolkit 200+ financial ratios, valuation models, risk metrics, and economic indicators. Works on stocks, ETFs, options, currencies, commodities, and crypto from Python. 9. Ghostfolio A private wealth dashboard for stocks, ETFs, and crypto across all your accounts. Performance, allocation, diversification. Your data never leaves your machine. 10. OpenTerminalUI A self-hosted trading terminal: pro charts, screeners, options chains with live Greeks, portfolio optimization, backtesting, and an AI research agent. Runs entirely on your own hardware. Bloomberg spent 40 years building a $27,000/year moat. Open source is draining it one repo at a time. The software is free. Some live data feeds need your own API keys, but the barrier is now effort, not money. If you want the exact workflows we use to stack these tools with AI, join the AIBullss Discord:
0:13

Goldman pays $27,000 per seat for a Bloomberg Terminal. I found 10 open source tools on GitHub that replicate almost all of it for free. Retail investors have never had this much firepower. Bookmark & Repost this one: 1. OpenBB Stocks, options, crypto, forex, and macro data in one research platform. Build your own dashboards, reports, and AI analysts on top of it. The OG of open source finance. 50K+ stars. 2. FinceptTerminal A full financial terminal: global market data, advanced charts, economic indicators, portfolio analysis, and AI research tools. Windows, Mac, and Linux. 3. Neuberg 516 drag-and-drop panels covering equities, bonds, commodities, currencies, credit, and macro. Even connects to Alpaca, Hyperliquid, and Polymarket so you can trade from the terminal itself. 4. Qlib (by Microsoft) An open source AI platform for quant investing. Train ML models, discover signals, backtest strategies, and build portfolios with the same workflow a quant desk uses. 5. FinRobot An AI equity research team on your laptop. Its agents read financial statements, build DCF valuations, debate bull vs bear cases, and generate full investment reports. 6. EdgarTools Turns the SEC database into something humans can actually use. Pull 10-Ks, 10-Qs, insider trades, executive pay, and hedge fund holdings going back to 1994. 7. LEAN (by QuantConnect) An institutional-grade engine for trading algorithms. Write strategies in Python or C#, backtest on decades of data, then connect to real brokers and go live. 8. FinanceToolkit 200+ financial ratios, valuation models, risk metrics, and economic indicators. Works on stocks, ETFs, options, currencies, commodities, and crypto from Python. 9. Ghostfolio A private wealth dashboard for stocks, ETFs, and crypto across all your accounts. Performance, allocation, diversification. Your data never leaves your machine. 10. OpenTerminalUI A self-hosted trading terminal: pro charts, screeners, options chains with live Greeks, portfolio optimization, backtesting, and an AI research agent. Runs entirely on your own hardware. Bloomberg spent 40 years building a $27,000/year moat. Open source is draining it one repo at a time. The software is free. Some live data feeds need your own API keys, but the barrier is now effort, not money. If you want the exact workflows we use to stack these tools with AI, join the AIBullss Discord:

AlphaCartel

22,101 Aufrufe • vor 2 Monaten

Impeccable 3.7 brings linting to design. Until now it was a skill you asked for help. Now it's a design-system-aware feedback loop that runs while your agent builds, catching slop and design drift before they land. 🪝 Design hooks for Claude, Codex, and Cursor They run after every UI edit and quietly nudge your agent to fix slop and drift. The output isn't another wall of lint: it separates new findings from already-seen ones, flags clean scans, and asks the agent to use judgment. Fix real issues, leave intentional demos alone, save exceptions to config instead of littering your source. 🎨 Slop detection is now project-aware Reads your actual design system from DESIGN.md, your typography, palette, radius scale, and tokens, and flags drift from your system, not just generic AI slop: • this font isn't in your design system • this color is outside your documented palette • this radius doesn't match your rounded scale The same engine powers both the hooks and the CLI, and it's where we're investing next. 🖥️ Live Mode, ready for real projects Svelte/SvelteKit now preview variants as temporary framework components with live params, then accept cleanly back into your source component. Manual text edits got evidence / apply / discard routes, insertions preserve their anchors, and mapped lists and JSX slots clean up far more reliably. ⚡ Leaner core, sharper detector Rule-level evals across 3 providers and 4 niches cut guidance with no measurable lift and dropped examples that taught models bad patterns. The detector now skips hidden and screen-reader-only elements, understands OKLCH alpha and Sass-like inputs, and tightened checks for repeated kickers, oversized H1s, clipped overflow, and cramped padding. 🛠️ CLI caught up impeccable detect loads DESIGN.md by default, motion findings name the exact token or cubic-bezier instead of just "bounce," and impeccable ignores gives real CRUD for exceptions. Hooks and CLI share the same ignores. No split-brain config. Plus a much-improved interactive installer with hooks setup built in. Upgrade: npx impeccable install npm i -g impeccable
0:12

Impeccable 3.7 brings linting to design. Until now it was a skill you asked for help. Now it's a design-system-aware feedback loop that runs while your agent builds, catching slop and design drift before they land. 🪝 Design hooks for Claude, Codex, and Cursor They run after every UI edit and quietly nudge your agent to fix slop and drift. The output isn't another wall of lint: it separates new findings from already-seen ones, flags clean scans, and asks the agent to use judgment. Fix real issues, leave intentional demos alone, save exceptions to config instead of littering your source. 🎨 Slop detection is now project-aware Reads your actual design system from DESIGN.md, your typography, palette, radius scale, and tokens, and flags drift from your system, not just generic AI slop: • this font isn't in your design system • this color is outside your documented palette • this radius doesn't match your rounded scale The same engine powers both the hooks and the CLI, and it's where we're investing next. 🖥️ Live Mode, ready for real projects Svelte/SvelteKit now preview variants as temporary framework components with live params, then accept cleanly back into your source component. Manual text edits got evidence / apply / discard routes, insertions preserve their anchors, and mapped lists and JSX slots clean up far more reliably. ⚡ Leaner core, sharper detector Rule-level evals across 3 providers and 4 niches cut guidance with no measurable lift and dropped examples that taught models bad patterns. The detector now skips hidden and screen-reader-only elements, understands OKLCH alpha and Sass-like inputs, and tightened checks for repeated kickers, oversized H1s, clipped overflow, and cramped padding. 🛠️ CLI caught up impeccable detect loads DESIGN.md by default, motion findings name the exact token or cubic-bezier instead of just "bounce," and impeccable ignores gives real CRUD for exceptions. Hooks and CLI share the same ignores. No split-brain config. Plus a much-improved interactive installer with hooks setup built in. Upgrade: npx impeccable install npm i -g impeccable

Impeccable

233,051 Aufrufe • vor 4 Monaten

I just built a Claude skill that audits your entire Meta Ads account in under 5 minutes 🤯 Export your CSV from Ads Manager → drop it into Claude → get back an account health score, a wasted spend breakdown, and a prioritized fix list telling you exactly what to change this week. All inside Claude Cowork. Perfect for DTC brands and agencies who are running Meta Ads but have no idea which creatives are bleeding budget, which audiences stopped converting, or why CPA crept up 40% last month. If your weekly Meta workflow still looks like this — open Ads Manager, stare at the dashboard, sort by spend, squint at CTR columns, export a CSV you never actually analyze, close the tab and hope for the best... This skill runs the full audit for you: → Reads your Meta Ads CSV export (campaign, ad set, and ad-level data) → Scores your account 0-100 across 6 dimensions: creative health, audience efficiency, budget allocation, funnel performance, fatigue signals, and offer effectiveness → Calculates your exact wasted spend in dollars: every ad with spend and zero purchases → Identifies creative fatigue before it tanks your CPA (declining CTR + rising frequency + increasing cost) → Flags audience overlap and saturation across ad sets → Delivers a top-5 fix list ranked by how much money each fix saves you No API connection. No third-party tool access to your ad account. No risk of Meta flagging your account. What you get: →A full account health score (0-100) with a grade for each dimension →Your exact wasted spend in dollars (not a vague "you're overspending") →Creative fatigue signals with specific ads to kill or refresh this week →Audience efficiency analysis showing which ad sets are cannibalizing each other →A prioritized fix list ranked by budget impact (do #1 first, save the most money) One CSV export, one prompt. Five minutes. I put together the full playbook with the skill file, the scoring methodology, and the exact CSV export steps from Ads Manager. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)
0:10

I just built a Claude skill that audits your entire Meta Ads account in under 5 minutes 🤯 Export your CSV from Ads Manager → drop it into Claude → get back an account health score, a wasted spend breakdown, and a prioritized fix list telling you exactly what to change this week. All inside Claude Cowork. Perfect for DTC brands and agencies who are running Meta Ads but have no idea which creatives are bleeding budget, which audiences stopped converting, or why CPA crept up 40% last month. If your weekly Meta workflow still looks like this — open Ads Manager, stare at the dashboard, sort by spend, squint at CTR columns, export a CSV you never actually analyze, close the tab and hope for the best... This skill runs the full audit for you: → Reads your Meta Ads CSV export (campaign, ad set, and ad-level data) → Scores your account 0-100 across 6 dimensions: creative health, audience efficiency, budget allocation, funnel performance, fatigue signals, and offer effectiveness → Calculates your exact wasted spend in dollars: every ad with spend and zero purchases → Identifies creative fatigue before it tanks your CPA (declining CTR + rising frequency + increasing cost) → Flags audience overlap and saturation across ad sets → Delivers a top-5 fix list ranked by how much money each fix saves you No API connection. No third-party tool access to your ad account. No risk of Meta flagging your account. What you get: →A full account health score (0-100) with a grade for each dimension →Your exact wasted spend in dollars (not a vague "you're overspending") →Creative fatigue signals with specific ads to kill or refresh this week →Audience efficiency analysis showing which ad sets are cannibalizing each other →A prioritized fix list ranked by budget impact (do #1 first, save the most money) One CSV export, one prompt. Five minutes. I put together the full playbook with the skill file, the scoring methodology, and the exact CSV export steps from Ads Manager. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

Mike Futia

36,237 Aufrufe • vor 6 Monaten

i was genuinely shocked to find out that most people still aren't using some of the most basic Claude Code features here's the setup I use with three simple primitives that will speed up your work by 100x: /routines -> the rhythm cloud cron for your agent. "every day at 6am", "every monday at 9am". it runs whether your laptop is open or not /loop -> the short-term watch repeats a prompt every X minutes, hours, or days - whatever you need. built for monitoring /goal -> the quality gate you define what "done" actually means, and a second agent audits whether the condition is truly met before it's allowed to stop best part is it doesn't quit after the first try... it keeps fixing until the bar is hit here's some real usecases: /routines (long-term, hands-off): > daily 6am -> sweep X, reddit and the top newsletters for fresh AI news, sort every find into the right topic folder with a one-line summary > daily 9pm -> read everything i saved today and write me a clean end-of-day summary: what matters, what to ignore, what to act on > every trading day 7am -> pull overnight moves + catalysts, build the summary sheet for traders (ticker, setup, key level, note) and drop it in reports /goal (a finish condition that can't be faked): > "refactor module X" -> won't stop until every test passes and the build is green > "no english text left on checkout.html and grep finds zero instances of 'reading', 'diagnosis', 'cards'" -> cleans legacy funnel residue and verifies itself > "competitor report has min 10 Meta Ad Library ads, each with hook + CTA broken down" -> research that doesn't stop at 3 finds /loop (short cyclical monitoring): > every 10m -> check the Netlify deploy, if it failed read the log and fix the cause > every 30m -> scan Stripe webhooks in the function logs, diagnose any 4xx/5xx > every 1h -> pull campaign results from Meta Ads API, append to csv, ping me if CPA crosses $15 you can also use full combo for single tasks: /routines fires it at 6am -> the /goal condition says "not done until the report pulls all 3 sources and is saved to reports"
0:14

i was genuinely shocked to find out that most people still aren't using some of the most basic Claude Code features here's the setup I use with three simple primitives that will speed up your work by 100x: /routines -> the rhythm cloud cron for your agent. "every day at 6am", "every monday at 9am". it runs whether your laptop is open or not /loop -> the short-term watch repeats a prompt every X minutes, hours, or days - whatever you need. built for monitoring /goal -> the quality gate you define what "done" actually means, and a second agent audits whether the condition is truly met before it's allowed to stop best part is it doesn't quit after the first try... it keeps fixing until the bar is hit here's some real usecases: /routines (long-term, hands-off): > daily 6am -> sweep X, reddit and the top newsletters for fresh AI news, sort every find into the right topic folder with a one-line summary > daily 9pm -> read everything i saved today and write me a clean end-of-day summary: what matters, what to ignore, what to act on > every trading day 7am -> pull overnight moves + catalysts, build the summary sheet for traders (ticker, setup, key level, note) and drop it in reports /goal (a finish condition that can't be faked): > "refactor module X" -> won't stop until every test passes and the build is green > "no english text left on checkout.html and grep finds zero instances of 'reading', 'diagnosis', 'cards'" -> cleans legacy funnel residue and verifies itself > "competitor report has min 10 Meta Ad Library ads, each with hook + CTA broken down" -> research that doesn't stop at 3 finds /loop (short cyclical monitoring): > every 10m -> check the Netlify deploy, if it failed read the log and fix the cause > every 30m -> scan Stripe webhooks in the function logs, diagnose any 4xx/5xx > every 1h -> pull campaign results from Meta Ads API, append to csv, ping me if CPA crosses $15 you can also use full combo for single tasks: /routines fires it at 6am -> the /goal condition says "not done until the report pulls all 3 sources and is saved to reports"

MAX

16,983 Aufrufe • vor 2 Monaten

🚨MASSIVE BREAKING : The Hindu Just drop Biggest BOMB on Traitor Gyanesh & dictator Modi🔥 (MUST Read & Share) India's voter list now has an ON/OFF switch. And it sits in Delhi, not in your state. → ECINet is ECI's one portal for everything: voter list, SIR forms, officer logins. → An audit of its code by an IIT Delhi-IIM Calcutta data scientist found a built-in "disable" button for every State Nodal Officer. → The pop-up literally says: "the user will no longer be able to access the application." → Viewing voters' SIR forms can be switched on or off, state by state. → The code even has a ready message: "not enabled for selected State". → And in West Bengal alone, the officers responsible for the voter list are blocked from these forms. And the joke doesn't end there. → How did the audit find all this? Not by hacking. → ECINet's login page loads a big code file before anyone even logs in. → Menu names, screens and API paths meant only for officials are sitting in it, readable by anyone. → The platform guarding almost 100 crore voters' data can't even hide its own officials-only menu. Now the bigger shock. This warning didn't come only from outside. It came from inside ECI. → Election Commissioners Sukhbir Singh Sandhu and Vivek Joshi wrote against the "gradual centralisation of the electoral roll database". → Sandhu wrote that he came to know about new ECINet modules "from media only". → Joshi asked for an outside IIT expert to audit who can change the voter database. → Finally, the two ECs wrote to the Cabinet Secretary. Now connect the dots. By law, the ERO in your area decides whose name stays on the voter list. On ECINet, what the ERO can actually do is decided by the IT team in Delhi. Law says decentralised. Software says one switch. Bro, imagine. Your society has a watchman, but the main gate lock is controlled by someone sitting in another city. He can lock any building he wants, anytime. And when two other committee members ask "who has the key?", nobody answers. That's not digital transformation. That's a remote control on democracy. Gyanesh , if two of your own Commissioners couldn't get answers, how will the voter? Modi, who holds the switch, and whom does he report to? 🔥🔥🔥
0:14

🚨MASSIVE BREAKING : The Hindu Just drop Biggest BOMB on Traitor Gyanesh & dictator Modi🔥 (MUST Read & Share) India's voter list now has an ON/OFF switch. And it sits in Delhi, not in your state. → ECINet is ECI's one portal for everything: voter list, SIR forms, officer logins. → An audit of its code by an IIT Delhi-IIM Calcutta data scientist found a built-in "disable" button for every State Nodal Officer. → The pop-up literally says: "the user will no longer be able to access the application." → Viewing voters' SIR forms can be switched on or off, state by state. → The code even has a ready message: "not enabled for selected State". → And in West Bengal alone, the officers responsible for the voter list are blocked from these forms. And the joke doesn't end there. → How did the audit find all this? Not by hacking. → ECINet's login page loads a big code file before anyone even logs in. → Menu names, screens and API paths meant only for officials are sitting in it, readable by anyone. → The platform guarding almost 100 crore voters' data can't even hide its own officials-only menu. Now the bigger shock. This warning didn't come only from outside. It came from inside ECI. → Election Commissioners Sukhbir Singh Sandhu and Vivek Joshi wrote against the "gradual centralisation of the electoral roll database". → Sandhu wrote that he came to know about new ECINet modules "from media only". → Joshi asked for an outside IIT expert to audit who can change the voter database. → Finally, the two ECs wrote to the Cabinet Secretary. Now connect the dots. By law, the ERO in your area decides whose name stays on the voter list. On ECINet, what the ERO can actually do is decided by the IT team in Delhi. Law says decentralised. Software says one switch. Bro, imagine. Your society has a watchman, but the main gate lock is controlled by someone sitting in another city. He can lock any building he wants, anytime. And when two other committee members ask "who has the key?", nobody answers. That's not digital transformation. That's a remote control on democracy. Gyanesh , if two of your own Commissioners couldn't get answers, how will the voter? Modi, who holds the switch, and whom does he report to? 🔥🔥🔥

The Rebel

217,219 Aufrufe • vor 3 Tagen

Claude Code + Meta Ads CLI is f*cking wild 🤯 I just replaced 80% of my Meta Ads reporting workflow inside Claude Code. All without logging into Ads Manager. Perfect for DTC brands and creative agencies who are sick of logging into Ads Manager, exporting CSVs, and rebuilding the same dashboards every Monday. This setup eliminates the entire loop: → Plug Meta's official Ads CLI into Claude Code → Type one sentence describing the report you want → Claude pulls the data, builds the artifact, and saves it to your folder → HTML dashboards, comparison tables, written briefs → Run it weekly, monthly, anytime a client asks for something custom No CSV exports. No Looker setup. No copy-pasting numbers into decks. What you get: → A live dashboard with KPI cards, top 10 ad set ranking, daily spend chart, and sortable table — built in 90 seconds → Week-over-week comparison reports with CTR drops and CPC spikes flagged automatically → Creative fatigue audits that flag dying ads before CPAs blow up → One-page executive briefs with winners, losers, action items, and recommendations → Anomaly reports that surface every metric deviation over 25% Built 100% in Claude Code with the official Meta CLI. No third-party connector means no ban risk. I put together the complete playbook with the 15-minute setup and a step-by-step Loom video showing you the full install. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)
0:12

Claude Code + Meta Ads CLI is f*cking wild 🤯 I just replaced 80% of my Meta Ads reporting workflow inside Claude Code. All without logging into Ads Manager. Perfect for DTC brands and creative agencies who are sick of logging into Ads Manager, exporting CSVs, and rebuilding the same dashboards every Monday. This setup eliminates the entire loop: → Plug Meta's official Ads CLI into Claude Code → Type one sentence describing the report you want → Claude pulls the data, builds the artifact, and saves it to your folder → HTML dashboards, comparison tables, written briefs → Run it weekly, monthly, anytime a client asks for something custom No CSV exports. No Looker setup. No copy-pasting numbers into decks. What you get: → A live dashboard with KPI cards, top 10 ad set ranking, daily spend chart, and sortable table — built in 90 seconds → Week-over-week comparison reports with CTR drops and CPC spikes flagged automatically → Creative fatigue audits that flag dying ads before CPAs blow up → One-page executive briefs with winners, losers, action items, and recommendations → Anomaly reports that surface every metric deviation over 25% Built 100% in Claude Code with the official Meta CLI. No third-party connector means no ban risk. I put together the complete playbook with the 15-minute setup and a step-by-step Loom video showing you the full install. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

Mike Futia

23,903 Aufrufe • vor 5 Monaten

The agents that run my LinkedIn outreach are currently getting a 55% reply rate on cold outreach and are generating hundreds of signups for me completely on autopilot. I packaged the exact system into a skill that works for any harness so you can copy it for yourself. Heres why this system prints qualified replies: most outbound starts with a list out of apollo, zoominfo or clay. firmographics. who could buy. that list is cold on arrival because "could buy" and "is thinking about this right now" are two different lists. so mine starts from linkedin posts my buyers are already reacting to. that also means every person on the list actually uses linkedin, since they just reacted to a post on it. most people don't, so a message to a normal list mostly sits unread. across the client accounts i've worked on, an apollo-style list usually gets about 1/10th the reply rate of one built this way. the posts come from three places: buyers posting about the problem themselves (hiring for it, breaking under it, shopping for a fix), a watchlist of my competitors and the creators my buyers follow, and the topic itself. when a buyer writes the post, the author is the best lead on it. anyone reacting to a competitor's product post is already shopping. every search is written in the words buyers actually post in, because vendor jargon only finds other vendors. it pulls everyone who reacted or commented, drops anyone i've already contacted plus competitors and their staff, and scores each person 0 to 100 against my ICP with the reason written on the row. anyone under the bar is gone before i've paid for their email. this is the part everyone skips. for every lead that clears the bar, it reads their website, their LinkedIn profile & anything else it can find about them on the internet, works out what agent would actually help their operation, and builds it. a real one, sitting there, with a claim link. so i am not sending "quick question about your outbound." i am sending "i built you an agent that does X for your company, want me to send over the link?" it sequences that into heyreach for linkedin and instantly for email, then runs the inbox. anyone who has done outbound knows reply speed is most of the game, and also the exact thing you quit doing by week three. every reply fires a webhook. it re-reads the whole thread, tags it interested, not interested or auto-reply, pings me in slack the second someone's interested, and sends them their claim link. i don't touch it. and every reply feeds the next run. it writes more searches like the ones that got answers, so week four sources better than week one. the general rule, and it has nothing to do with my software: use AI to build a personalized, genuinely useful asset and give it away in the first message. you're not hard selling anyone. you are handing them a thing. and because it's built off their actual company, nobody can mistake it for a template. reply rates go up for both reasons at once. it doesn't have to be an agent. the skill builds whatever your business is already good at making: - sell lead gen? a sample list of 10 to 25 of their own buyers - sell SEO? the 3 to 5 searches their competitors win and they don't - build websites? a teardown of their homepage with three fixes - consult? a one-page plan for the exact problem they commented about the playbook behind the messages is the best outbound advice on the internet in one place. it's built from 25 full-length videos and 60 posts from operators who do this at scale, plus 3 production systems running this exact play. scraped the videos and posts with superagnt's data MCP btw. run it once in claude code or codex and look at what it staged. once you like it, it turns the whole thing into a small agent team on a schedule. the sourcing side runs about 8 cents per qualified lead in production. grab it here, paste the install prompt into your agent and let it cook:
0:14

The agents that run my LinkedIn outreach are currently getting a 55% reply rate on cold outreach and are generating hundreds of signups for me completely on autopilot. I packaged the exact system into a skill that works for any harness so you can copy it for yourself. Heres why this system prints qualified replies: most outbound starts with a list out of apollo, zoominfo or clay. firmographics. who could buy. that list is cold on arrival because "could buy" and "is thinking about this right now" are two different lists. so mine starts from linkedin posts my buyers are already reacting to. that also means every person on the list actually uses linkedin, since they just reacted to a post on it. most people don't, so a message to a normal list mostly sits unread. across the client accounts i've worked on, an apollo-style list usually gets about 1/10th the reply rate of one built this way. the posts come from three places: buyers posting about the problem themselves (hiring for it, breaking under it, shopping for a fix), a watchlist of my competitors and the creators my buyers follow, and the topic itself. when a buyer writes the post, the author is the best lead on it. anyone reacting to a competitor's product post is already shopping. every search is written in the words buyers actually post in, because vendor jargon only finds other vendors. it pulls everyone who reacted or commented, drops anyone i've already contacted plus competitors and their staff, and scores each person 0 to 100 against my ICP with the reason written on the row. anyone under the bar is gone before i've paid for their email. this is the part everyone skips. for every lead that clears the bar, it reads their website, their LinkedIn profile & anything else it can find about them on the internet, works out what agent would actually help their operation, and builds it. a real one, sitting there, with a claim link. so i am not sending "quick question about your outbound." i am sending "i built you an agent that does X for your company, want me to send over the link?" it sequences that into heyreach for linkedin and instantly for email, then runs the inbox. anyone who has done outbound knows reply speed is most of the game, and also the exact thing you quit doing by week three. every reply fires a webhook. it re-reads the whole thread, tags it interested, not interested or auto-reply, pings me in slack the second someone's interested, and sends them their claim link. i don't touch it. and every reply feeds the next run. it writes more searches like the ones that got answers, so week four sources better than week one. the general rule, and it has nothing to do with my software: use AI to build a personalized, genuinely useful asset and give it away in the first message. you're not hard selling anyone. you are handing them a thing. and because it's built off their actual company, nobody can mistake it for a template. reply rates go up for both reasons at once. it doesn't have to be an agent. the skill builds whatever your business is already good at making: - sell lead gen? a sample list of 10 to 25 of their own buyers - sell SEO? the 3 to 5 searches their competitors win and they don't - build websites? a teardown of their homepage with three fixes - consult? a one-page plan for the exact problem they commented about the playbook behind the messages is the best outbound advice on the internet in one place. it's built from 25 full-length videos and 60 posts from operators who do this at scale, plus 3 production systems running this exact play. scraped the videos and posts with superagnt's data MCP btw. run it once in claude code or codex and look at what it staged. once you like it, it turns the whole thing into a small agent team on a schedule. the sourcing side runs about 8 cents per qualified lead in production. grab it here, paste the install prompt into your agent and let it cook:

Jáen

675,666 Aufrufe • vor 1 Monat

A good technical LLM interview question: Your RAG chatbot is working as expected locally. You deploy it behind a load balancer with 3 replicas. Users report that it forgets what they just asked, and answers get worse with each restart. Why did this happen? (answer below) A local setup has one process that owns everything. - The vector index is a variable in memory. - Conversation history is a Python list. - The documents are on local disk. You never treat any of them as infrastructure, because restarting rebuilds all three in seconds and there is only ever one copy. The setup does not carry over to production directly. The vector index might disappear on restart, so the app re-embeds everything on boot and serves empty results until it finishes. Conversation history may belong to one replica, so a follow-up routed elsewhere has no memory of the previous turn. Documents could be on whichever container ingested them, so the three replicas hold three different corpora. None of this is evident with one user and one process. So the actual work in shipping RAG is not just the retrieval logic, but also storing the vector index, the conversation history, and the documents outside the app, where every replica reads and writes the same copy. Which comes down to three requirements: > The vector store needs persistence and has to be reachable from every replica. pgvector inside Postgres keeps embeddings next to the rest of the data instead of adding another system to operate. > Conversation state has to be checkpointed outside the app. LangGraph writes its state to Postgres, so any replica can pick up a thread mid-conversation. > Docs need shared object storage, so ingestion happens once instead of once per replica. If you get those three right, the retrieval logic you wrote in the notebook works unchanged. To learn how all of it is wired together, Akamai's GitHub has a working reference implementation. - rag-langgraph-k8s-quickstart is an airline policy Q&A assistant built with FastAPI, LangChain, and LangGraph. Terraform provisions the LKE cluster, a Postgres instance with pgvector for embeddings, a second Postgres for LangGraph checkpointing, and an object storage bucket for the policy documents, in one apply. - akamai-workshop-ai-inference covers the next step, running the model yourself instead of calling an API, with prefill and decode, KV cache tradeoffs, and continuous batching under real concurrency. Both are available on Akamai's new Developer Hub, alongside their tutorials and code samples. It also links to Edge Case, their Discord, where four developer advocates architect and deploy a production app live every other Wednesday. If you create a new Akamai Cloud account, you can also get $300 in credits for joining. Join here: That said, this post assumes the retrieval logic was right to begin with, and that is doing a lot of work. Most RAG systems fail earlier, at the point where a chunk gets treated as a self-contained unit of meaning. I wrote about the two skills that fix that gap, and why the chunk is usually the wrong thing to embed. Read it below. Thanks to Akamai Cloud for partnering today!
0:14

A good technical LLM interview question: Your RAG chatbot is working as expected locally. You deploy it behind a load balancer with 3 replicas. Users report that it forgets what they just asked, and answers get worse with each restart. Why did this happen? (answer below) A local setup has one process that owns everything. - The vector index is a variable in memory. - Conversation history is a Python list. - The documents are on local disk. You never treat any of them as infrastructure, because restarting rebuilds all three in seconds and there is only ever one copy. The setup does not carry over to production directly. The vector index might disappear on restart, so the app re-embeds everything on boot and serves empty results until it finishes. Conversation history may belong to one replica, so a follow-up routed elsewhere has no memory of the previous turn. Documents could be on whichever container ingested them, so the three replicas hold three different corpora. None of this is evident with one user and one process. So the actual work in shipping RAG is not just the retrieval logic, but also storing the vector index, the conversation history, and the documents outside the app, where every replica reads and writes the same copy. Which comes down to three requirements: > The vector store needs persistence and has to be reachable from every replica. pgvector inside Postgres keeps embeddings next to the rest of the data instead of adding another system to operate. > Conversation state has to be checkpointed outside the app. LangGraph writes its state to Postgres, so any replica can pick up a thread mid-conversation. > Docs need shared object storage, so ingestion happens once instead of once per replica. If you get those three right, the retrieval logic you wrote in the notebook works unchanged. To learn how all of it is wired together, Akamai's GitHub has a working reference implementation. - rag-langgraph-k8s-quickstart is an airline policy Q&A assistant built with FastAPI, LangChain, and LangGraph. Terraform provisions the LKE cluster, a Postgres instance with pgvector for embeddings, a second Postgres for LangGraph checkpointing, and an object storage bucket for the policy documents, in one apply. - akamai-workshop-ai-inference covers the next step, running the model yourself instead of calling an API, with prefill and decode, KV cache tradeoffs, and continuous batching under real concurrency. Both are available on Akamai's new Developer Hub, alongside their tutorials and code samples. It also links to Edge Case, their Discord, where four developer advocates architect and deploy a production app live every other Wednesday. If you create a new Akamai Cloud account, you can also get $300 in credits for joining. Join here: That said, this post assumes the retrieval logic was right to begin with, and that is doing a lot of work. Most RAG systems fail earlier, at the point where a chunk gets treated as a self-contained unit of meaning. I wrote about the two skills that fix that gap, and why the chunk is usually the wrong thing to embed. Read it below. Thanks to Akamai Cloud for partnering today!

Akshay 🚀

31,971 Aufrufe • vor 1 Monat

The largest theft in history has already happened. The people behind it just cannot open what they stole yet. Right now, intelligence agencies and criminal groups are quietly copying the world's encrypted data, bank records, medical files, state secrets, private messages, and storing every byte untouched. They cannot read any of it. They are collecting it anyway, because they know the key is about to be invented. The strategy has a name, harvest now, decrypt later, and in 2026 it stopped being theory. Washington declared this the Year of Quantum Security in January, backed by the FBI, the NSA, and NIST. Canada ordered every federal agency to file a migration plan by April. Europe set its deadline for December. Governments do not impose operational deadlines on a someday problem. They do it when the clock is already running. Here is what moved the clock. Every password, every transfer, every secret on Earth is protected by one assumption, that a certain math problem is too hard to solve. Quantum computers solve exactly that problem. For years the machine that could do it looked decades away. Then in late 2025 Google's Willow chip cracked the hardest part of building one, and in March 2026 Google's own researchers estimated that breaking the encryption behind Bitcoin might take fewer than 500,000 qubits, down from 20 million, and could run in minutes. The day this becomes real has a name, Q-Day, and the latest estimates place it between 2030 and 2033. Now make it concrete. Roughly 6.5 million Bitcoin, about a third of every coin that will ever exist, worth close to 500 billion dollars, sit in addresses that have already exposed the very key a quantum computer needs. That includes the coins of Satoshi, the anonymous creator. On Q-Day they become, in the researchers' own word, trivially stealable. It would not look like a crash or a whale selling. It would look like half a trillion dollars of the most secure money ever built simply walking out the door. The asset designed to trust no one and no institution turns out to rest on a single unverified bet, that one math problem stays hard forever. This is what sits beneath the entire digital world. A bank balance, a Bitcoin, a classified cable, all of it is real only because of a proof you supposedly cannot forge. Quantum breaks the proof. Everything we call secure is true only until someone finally checks, and for the first time the check is visible on the horizon. You cannot know whether your data has already been copied. You cannot know the exact day the key arrives. The trust holding up the digital age is a clock counting down to a zero no one can see. The honest counter matters. No machine on Earth can break this encryption today, and serious cryptographers still argue the real threat is a decade or more away. The timeline is far from certain. Quantum-safe codes already exist, the migration has started, and Bitcoin can move its coins to safety before Q-Day if it acts in time. The danger is not that everything breaks tomorrow. It is that anything which must stay secret into the 2030s, a state secret, an identity, a private key, is being stolen today and is already on the clock. The breach is not coming. It is already here, sitting in storage, perfectly encrypted, waiting for a machine that does not exist yet to read it out loud. Research and opinion, not investment advice.
0:10

The largest theft in history has already happened. The people behind it just cannot open what they stole yet. Right now, intelligence agencies and criminal groups are quietly copying the world's encrypted data, bank records, medical files, state secrets, private messages, and storing every byte untouched. They cannot read any of it. They are collecting it anyway, because they know the key is about to be invented. The strategy has a name, harvest now, decrypt later, and in 2026 it stopped being theory. Washington declared this the Year of Quantum Security in January, backed by the FBI, the NSA, and NIST. Canada ordered every federal agency to file a migration plan by April. Europe set its deadline for December. Governments do not impose operational deadlines on a someday problem. They do it when the clock is already running. Here is what moved the clock. Every password, every transfer, every secret on Earth is protected by one assumption, that a certain math problem is too hard to solve. Quantum computers solve exactly that problem. For years the machine that could do it looked decades away. Then in late 2025 Google's Willow chip cracked the hardest part of building one, and in March 2026 Google's own researchers estimated that breaking the encryption behind Bitcoin might take fewer than 500,000 qubits, down from 20 million, and could run in minutes. The day this becomes real has a name, Q-Day, and the latest estimates place it between 2030 and 2033. Now make it concrete. Roughly 6.5 million Bitcoin, about a third of every coin that will ever exist, worth close to 500 billion dollars, sit in addresses that have already exposed the very key a quantum computer needs. That includes the coins of Satoshi, the anonymous creator. On Q-Day they become, in the researchers' own word, trivially stealable. It would not look like a crash or a whale selling. It would look like half a trillion dollars of the most secure money ever built simply walking out the door. The asset designed to trust no one and no institution turns out to rest on a single unverified bet, that one math problem stays hard forever. This is what sits beneath the entire digital world. A bank balance, a Bitcoin, a classified cable, all of it is real only because of a proof you supposedly cannot forge. Quantum breaks the proof. Everything we call secure is true only until someone finally checks, and for the first time the check is visible on the horizon. You cannot know whether your data has already been copied. You cannot know the exact day the key arrives. The trust holding up the digital age is a clock counting down to a zero no one can see. The honest counter matters. No machine on Earth can break this encryption today, and serious cryptographers still argue the real threat is a decade or more away. The timeline is far from certain. Quantum-safe codes already exist, the migration has started, and Bitcoin can move its coins to safety before Q-Day if it acts in time. The danger is not that everything breaks tomorrow. It is that anything which must stay secret into the 2030s, a state secret, an identity, a private key, is being stolen today and is already on the clock. The breach is not coming. It is already here, sitting in storage, perfectly encrypted, waiting for a machine that does not exist yet to read it out loud. Research and opinion, not investment advice.

Shanaka Anslem Perera ⚡

185,548 Aufrufe • vor 3 Monaten

Top 12 agentic use cases for Jev: (bookmark this) Jev handles semantic decisions that ordinary code cannot express reliably. It returns typed answers and probabilities, while code continues to cover the workflow. Here are 12 practical use cases for Jev: 1. Browser next action > Convert the current DOM state into a bounded action such as click, type, or stop. Code executes only valid operation-target pairs. There are already several open-source Jev web agents. 2. Context compaction > Decide which events from a long agent trace should remain. The selected text stays verbatim instead of being replaced with a generated summary. 3. Skill and context loading > Compare the current user turn against the available skills. Load only the instructions needed for that turn instead of filling the context window with every skill. 4. Typed tool-call compilation > Map a natural-language request to a function and fill its typed arguments. Each argument is evaluated separately before code allows execution. 5. Citation verification > Check whether a quoted passage exists and whether the surrounding evidence supports the claim. The output can be supported, unsupported, or contradicted. 6. Extraction verification > Run a cheap extractor first, then use Jev to verify questionable fields. Clean records stay on the fast path while uncertain ones reach a reasoning model. 7. Agent trace evaluation > Turn raw trajectories into queryable labels such as progress and repetition. This avoids asking another LLM to write a full review of every run. 8. Semantic regression tests > Replay a trace suite against a new agent build. Semantic checks can then pass or block prompt, model, tool, and policy changes in CI. 9. Jevgrep code search > Search a codebase by what the code does rather than its exact words. Jev scores candidate snippets and returns the most relevant code first. 10. Entity alignment > Compare two candidate records and decide whether to merge, review, or keep them separate. Candidate generation remains deterministic while Jev handles semantic identity. 11. Retrieval reranking > Let embeddings retrieve a broad candidate set, then use Jev to reorder passages by relevance. The generation model receives the most useful evidence first. 12. Memory promotion gate > Capture a completed agent trace, then judge whether its corrections contain a reusable lesson. Trace-backed lessons can be promoted while task-specific noise is discarded. If you want to see the final pattern in practice, it is already implemented in the Beacon open-source project. Beacon captures full sessions across Claude Code, Codex, Cursor, OpenCode, and 20+ agent harnesses, and then Jev identifies which workflows and corrections are worth learning from, so that a lesson discovered by one agent can become available to the others. GitHub repo: (don’t forget to star it ⭐) If you want to dive deeper, I also wrote about a similar mechanism in a hands-on guide. It covers building a Jev-style decision path with open models, entirely locally. Read it below.
0:16

Top 12 agentic use cases for Jev: (bookmark this) Jev handles semantic decisions that ordinary code cannot express reliably. It returns typed answers and probabilities, while code continues to cover the workflow. Here are 12 practical use cases for Jev: 1. Browser next action > Convert the current DOM state into a bounded action such as click, type, or stop. Code executes only valid operation-target pairs. There are already several open-source Jev web agents. 2. Context compaction > Decide which events from a long agent trace should remain. The selected text stays verbatim instead of being replaced with a generated summary. 3. Skill and context loading > Compare the current user turn against the available skills. Load only the instructions needed for that turn instead of filling the context window with every skill. 4. Typed tool-call compilation > Map a natural-language request to a function and fill its typed arguments. Each argument is evaluated separately before code allows execution. 5. Citation verification > Check whether a quoted passage exists and whether the surrounding evidence supports the claim. The output can be supported, unsupported, or contradicted. 6. Extraction verification > Run a cheap extractor first, then use Jev to verify questionable fields. Clean records stay on the fast path while uncertain ones reach a reasoning model. 7. Agent trace evaluation > Turn raw trajectories into queryable labels such as progress and repetition. This avoids asking another LLM to write a full review of every run. 8. Semantic regression tests > Replay a trace suite against a new agent build. Semantic checks can then pass or block prompt, model, tool, and policy changes in CI. 9. Jevgrep code search > Search a codebase by what the code does rather than its exact words. Jev scores candidate snippets and returns the most relevant code first. 10. Entity alignment > Compare two candidate records and decide whether to merge, review, or keep them separate. Candidate generation remains deterministic while Jev handles semantic identity. 11. Retrieval reranking > Let embeddings retrieve a broad candidate set, then use Jev to reorder passages by relevance. The generation model receives the most useful evidence first. 12. Memory promotion gate > Capture a completed agent trace, then judge whether its corrections contain a reusable lesson. Trace-backed lessons can be promoted while task-specific noise is discarded. If you want to see the final pattern in practice, it is already implemented in the Beacon open-source project. Beacon captures full sessions across Claude Code, Codex, Cursor, OpenCode, and 20+ agent harnesses, and then Jev identifies which workflows and corrections are worth learning from, so that a lesson discovered by one agent can become available to the others. GitHub repo: (don’t forget to star it ⭐) If you want to dive deeper, I also wrote about a similar mechanism in a hands-on guide. It covers building a Jev-style decision path with open models, entirely locally. Read it below.

Avi Chawla

129,018 Aufrufe • vor 27 Tagen

today was the first time i was genuinely impressed with what AI can do i recently decided to buy a whole FPV drone setup knowing basically nothing about the hardware side of it there's a pretty steep learning curve even just to set everything up properly: radios, RF protocols, flight controllers, ESCs, firmware, batteries, goggles, betaflight configs etc as someone that spends essentially 12h a day prompting agents to build software, it's actually pretty rare that i interact with AI on something where i have zero idea what's going on under the hood, and i never really used it for debugging a bunch of physical devices that all have to talk to each other i had codex + voice mode open for basically the entire setup. told it everything i bought, sent it some pics and then just started talking to it >what order do i set all this up in >how do i change this setting on the radio >which of these cables do i use >the drone is flashing pink wat mean >can you make this thing less insane to fly in my apartment and it was surprisingly seamless it would go find the manual for whatever specific thing i was holding, tell me exactly which buttons to press, what port to plug something into, what i should see if it worked etc then when i got to configuring the actual drone i had codex running on the computer it was plugged into, so it could inspect the config, back everything up, change settings, send usb reboot signals and check what happened the insane thing about voice mode is that youre literally hands on with the hardware and just telling codex what it should do, i literally never touched a thing on the computer besides starting voice mode if something doesn't work you tell it what happened and keep going a few hours of this and i had the radio, goggles, charger, batteries, drone firmware and betaflight all set up and had actually flown the thing the part that stuck with me is that i also understood what most of it was doing by the end, every time there was a term or tech i didnt understand id just ask to explain there is something absolutely magical about having proper real time personalized assistance, being able to dump a pile of unfamiliar hardware on your desk and have something figure out exactly what you own and walk through it with you in real time you become the missing physical link pressing the buttons i think spending all day using coding agents has actually made me pretty numb to AI progress. every new model is a bit better at some benchmark or can oneshot some task that the previous one couldn't and you just kinda adjust to it this felt different mostly because i had no existing knowledge to fall back on for the first time the jarvis comparison didn't feel cringe ai for coding and general computer tasks is cool and all but this feels a lot closer to the endgame anyone should be able to just ask any question about whats going on in their life and have realtime support i wonder if more hardware products will actually start exposing some sort of MCP or interface for agents to plug into thinking for example of how elevators in china are increasingly built with interfaces that let delivery robots call them directly instead of having to physically press a button we might actually start seeing hardware design shift from being purely human-interface-first to also being agent-interface-first buttons, screens and menus exist because humans need some way to tell machines what to do. agents don't necessarily need any of that if the hardware exposes an interface directly very curious which side closes the physical world gap first: humanoid robots that can operate hardware designed for humans, or hardware adapting so agents can operate it directly
0:18

today was the first time i was genuinely impressed with what AI can do i recently decided to buy a whole FPV drone setup knowing basically nothing about the hardware side of it there's a pretty steep learning curve even just to set everything up properly: radios, RF protocols, flight controllers, ESCs, firmware, batteries, goggles, betaflight configs etc as someone that spends essentially 12h a day prompting agents to build software, it's actually pretty rare that i interact with AI on something where i have zero idea what's going on under the hood, and i never really used it for debugging a bunch of physical devices that all have to talk to each other i had codex + voice mode open for basically the entire setup. told it everything i bought, sent it some pics and then just started talking to it >what order do i set all this up in >how do i change this setting on the radio >which of these cables do i use >the drone is flashing pink wat mean >can you make this thing less insane to fly in my apartment and it was surprisingly seamless it would go find the manual for whatever specific thing i was holding, tell me exactly which buttons to press, what port to plug something into, what i should see if it worked etc then when i got to configuring the actual drone i had codex running on the computer it was plugged into, so it could inspect the config, back everything up, change settings, send usb reboot signals and check what happened the insane thing about voice mode is that youre literally hands on with the hardware and just telling codex what it should do, i literally never touched a thing on the computer besides starting voice mode if something doesn't work you tell it what happened and keep going a few hours of this and i had the radio, goggles, charger, batteries, drone firmware and betaflight all set up and had actually flown the thing the part that stuck with me is that i also understood what most of it was doing by the end, every time there was a term or tech i didnt understand id just ask to explain there is something absolutely magical about having proper real time personalized assistance, being able to dump a pile of unfamiliar hardware on your desk and have something figure out exactly what you own and walk through it with you in real time you become the missing physical link pressing the buttons i think spending all day using coding agents has actually made me pretty numb to AI progress. every new model is a bit better at some benchmark or can oneshot some task that the previous one couldn't and you just kinda adjust to it this felt different mostly because i had no existing knowledge to fall back on for the first time the jarvis comparison didn't feel cringe ai for coding and general computer tasks is cool and all but this feels a lot closer to the endgame anyone should be able to just ask any question about whats going on in their life and have realtime support i wonder if more hardware products will actually start exposing some sort of MCP or interface for agents to plug into thinking for example of how elevators in china are increasingly built with interfaces that let delivery robots call them directly instead of having to physically press a button we might actually start seeing hardware design shift from being purely human-interface-first to also being agent-interface-first buttons, screens and menus exist because humans need some way to tell machines what to do. agents don't necessarily need any of that if the hardware exposes an interface directly very curious which side closes the physical world gap first: humanoid robots that can operate hardware designed for humans, or hardware adapting so agents can operate it directly

ultra

18,297 Aufrufe • vor 1 Monat

✨ I open sourced my first Chrome extension 🚀 SuperLevels I vibe coded it to replace all my Chrome extensions that are increasingly being bought up by spyware and malware companies who sell your data or worse hack your accounts and steal your stuff/money/data, which I'd call one of the top security risks right now For example: Chrome extensions can read your cookies or localStorage data, including session tokens, then login to your web or email accounts and hack you, they can inject code into any site to pull data form any site you browse, then break into your crypto accounts, drain your wallets, and selling your browsing history to ad companies, but that'd actually be the most favorable thing to happen of all these! Chrome extensions are just very very very unsafe So I coded my own, that I can trust because I made it, and I can read the source code: my extension is called 🚀SuperLevels and has all the features that the Chrome extensions I used to use have but all built into one safe one The cool thing is it's 100% open source and free, and you can audit the code first with AI yourself before installing it, and then if you do install it, customize it to your liking again with AI It has these features that improve my daily workflow while browsing the web: 🚮 Tab Cleaner Automatically closes inactive tabs after a configurable timeout (default: 5 minutes). Set excluded hosts to keep important tabs alive. View and re-open recently closed tabs. 🍪 Cookie Editor Full cookie manager for the current site. View, edit, add, and delete cookies. Export cookies as JSON. Expand any cookie to see and modify all fields including domain, path, SameSite, secure, and httpOnly flags. 🔀 Redirect Tracer See every redirect hop your browser took to reach the current page. Shows status codes (301, 302, 307, etc.) with a visual chain. Copy the full redirect chain to clipboard. 🌙 Dark Mode Instant dark mode for any website using CSS filter inversion. Adjustable brightness. Toggle per-site or globally. Images and videos are automatically re-inverted so they look normal. 𝕏 X Dim Mode Custom dim theme for X/Twitter with 7 color palettes: Dim, Slate, Jade, Plum, Dusk, Ember, or a custom hue. Live preview in the popup. ⚡ JS Toggle Disable JavaScript per-site with one click. Useful for debugging, reading articles without popups, or testing progressive enhancement. Page reloads automatically. 🚫 GDPR Cookie Consent Dismisser Auto-hides and auto-clicks cookie consent banners. Supports OneTrust, CookieBot, Didomi, Quantcast, GDPR plugins, and dozens more frameworks. Toggle off if a site breaks. 🎨 Live CSS Editor Write custom CSS for any website, applied in real-time as you type. Saved per-domain. Supports tab key for indentation. 📺 YouTube Unhook Removes YouTube distractions: no homepage feed, no sidebar suggestions, no end screen overlays, no Shorts. Search still works — just no algorithmic recommendations. 🎵 Music Recognizer Shazam-like music identification for any tab. Captures 10 seconds of audio and identifies the song via ACRCloud (free signup, bring your own API key). Results link to YouTube. History of recognized songs. 🖼 Picture-in-Picture Pop the largest video on the current tab into a floating PiP window with one click. 🗺 Google Maps Links Re-adds clickable Maps links and map preview cards to Google Search results. 🖼 View Image Adds a "View Image" button back to Google Images, linking directly to the full-size original image. {} JSON Formatter Auto-detects pure JSON response pages and formats them with syntax highlighting, collapsible sections, and a dark theme. Copy or view raw with one click. Never triggers on regular HTML pages.
0:17

✨ I open sourced my first Chrome extension 🚀 SuperLevels I vibe coded it to replace all my Chrome extensions that are increasingly being bought up by spyware and malware companies who sell your data or worse hack your accounts and steal your stuff/money/data, which I'd call one of the top security risks right now For example: Chrome extensions can read your cookies or localStorage data, including session tokens, then login to your web or email accounts and hack you, they can inject code into any site to pull data form any site you browse, then break into your crypto accounts, drain your wallets, and selling your browsing history to ad companies, but that'd actually be the most favorable thing to happen of all these! Chrome extensions are just very very very unsafe So I coded my own, that I can trust because I made it, and I can read the source code: my extension is called 🚀SuperLevels and has all the features that the Chrome extensions I used to use have but all built into one safe one The cool thing is it's 100% open source and free, and you can audit the code first with AI yourself before installing it, and then if you do install it, customize it to your liking again with AI It has these features that improve my daily workflow while browsing the web: 🚮 Tab Cleaner Automatically closes inactive tabs after a configurable timeout (default: 5 minutes). Set excluded hosts to keep important tabs alive. View and re-open recently closed tabs. 🍪 Cookie Editor Full cookie manager for the current site. View, edit, add, and delete cookies. Export cookies as JSON. Expand any cookie to see and modify all fields including domain, path, SameSite, secure, and httpOnly flags. 🔀 Redirect Tracer See every redirect hop your browser took to reach the current page. Shows status codes (301, 302, 307, etc.) with a visual chain. Copy the full redirect chain to clipboard. 🌙 Dark Mode Instant dark mode for any website using CSS filter inversion. Adjustable brightness. Toggle per-site or globally. Images and videos are automatically re-inverted so they look normal. 𝕏 X Dim Mode Custom dim theme for X/Twitter with 7 color palettes: Dim, Slate, Jade, Plum, Dusk, Ember, or a custom hue. Live preview in the popup. ⚡ JS Toggle Disable JavaScript per-site with one click. Useful for debugging, reading articles without popups, or testing progressive enhancement. Page reloads automatically. 🚫 GDPR Cookie Consent Dismisser Auto-hides and auto-clicks cookie consent banners. Supports OneTrust, CookieBot, Didomi, Quantcast, GDPR plugins, and dozens more frameworks. Toggle off if a site breaks. 🎨 Live CSS Editor Write custom CSS for any website, applied in real-time as you type. Saved per-domain. Supports tab key for indentation. 📺 YouTube Unhook Removes YouTube distractions: no homepage feed, no sidebar suggestions, no end screen overlays, no Shorts. Search still works — just no algorithmic recommendations. 🎵 Music Recognizer Shazam-like music identification for any tab. Captures 10 seconds of audio and identifies the song via ACRCloud (free signup, bring your own API key). Results link to YouTube. History of recognized songs. 🖼 Picture-in-Picture Pop the largest video on the current tab into a floating PiP window with one click. 🗺 Google Maps Links Re-adds clickable Maps links and map preview cards to Google Search results. 🖼 View Image Adds a "View Image" button back to Google Images, linking directly to the full-size original image. {} JSON Formatter Auto-detects pure JSON response pages and formats them with syntax highlighting, collapsible sections, and a dark theme. Copy or view raw with one click. Never triggers on regular HTML pages.

@levelsio

260,979 Aufrufe • vor 6 Monaten

Stanford researchers did it again. They just built the agent-native version of Git. When an agent works on a longer task, the run builds up a lot of state. This includes files edited/created, a dev server, a database, installed packages, KV cache, etc. Say the agent is at step 10 and makes a mistake, maybe it misreads a traceback and rewrites a file that was actually fine. The tests start failing, and the run goes off track, although everything through step eight was correct. By default, the agent just tries to fix it, which creates more edits and tool calls. This burns more tokens and grows the context. The other options are a person stepping in to redirect it or restarting the whole run from step one. That's wasteful, because it pays for every model/tool call again and re-prefills the context. Moreover, since an agent's run is non-deterministic, it doesn't reproduce the same early steps anyway. The reason it's hard to just jump back exactly to a previous correct step and resume from there is that the trajectory is only a message log. It records what the agent said and which tools it called, but not the live state underneath. That state includes things like memory, open file handles, child processes, installed packages, /tmp, and KV cache. None of that is in the log. Git can version the files, but it doesn't snapshot the running process or the KV cache. Checking out step eight moves the files back, but the process is still sitting in step-ten memory with a cold cache. Shepherd is a runtime layer by Stanford that records the run as a trace of typed events rather than a flat log. Each agent-environment interaction becomes a commit, similar to Git, but it tracks the live run. Its commit includes the agent process and the filesystem together, copy-on-write, so a branch carries the actual state and not just the files. Going back to a previous step is then a single call that forks from that commit and continues from the exact state. The copy-on-write fork is roughly five times faster than docker commit, and because the prompt prefix through step eight is unchanged, the KV cache is reused over 95% on replay, so early steps aren't reprocessed again. Once the run can be forked, a meta-agent can sit on top and operate it. It watches the trace and reverts as soon as it looks wrong, before the bad write is committed. In practice, it's just Python calling fork, replay, and revert on the trace, rather than a separate control plane wired into the harness. Not everything is reversible though. Files and sandbox changes undo themselves, but a database write has no automatic undo, so it needs a matching undo step set up in advance. Something external, like a sent email or a real charge, can't be undone, so the supervisor's job there is to catch it before it fires. They tested this on a few public benchmarks. On CooperBench, where two agents work on the same codebase, adding a live supervisor took the pair-coding pass rate from 28.8% to 54.7%. It's still early and labeled alpha. The benefit mostly shows up when a run gets branched a lot over a heavy sandbox state, which is exactly where restarting wastes the most tokens and time. If Git was made to make file changes reversible, Shepherd is trying to do the same thing for a live agent run. Shepherd Repo: (don't forget to star it ⭐ ) That said, Shepherd reverts a bad step inside a run. The harness around it, the prompts, tools, and checks the supervisor relies on, still drifts across runs as models and dependencies change. Akshay wrote about making that harness repair itself, where a failing trace gets diagnosed, the fix is verified against the exact input that failed, and the failure is locked as a regression test so it can't recur. Read it below.
0:15

Stanford researchers did it again. They just built the agent-native version of Git. When an agent works on a longer task, the run builds up a lot of state. This includes files edited/created, a dev server, a database, installed packages, KV cache, etc. Say the agent is at step 10 and makes a mistake, maybe it misreads a traceback and rewrites a file that was actually fine. The tests start failing, and the run goes off track, although everything through step eight was correct. By default, the agent just tries to fix it, which creates more edits and tool calls. This burns more tokens and grows the context. The other options are a person stepping in to redirect it or restarting the whole run from step one. That's wasteful, because it pays for every model/tool call again and re-prefills the context. Moreover, since an agent's run is non-deterministic, it doesn't reproduce the same early steps anyway. The reason it's hard to just jump back exactly to a previous correct step and resume from there is that the trajectory is only a message log. It records what the agent said and which tools it called, but not the live state underneath. That state includes things like memory, open file handles, child processes, installed packages, /tmp, and KV cache. None of that is in the log. Git can version the files, but it doesn't snapshot the running process or the KV cache. Checking out step eight moves the files back, but the process is still sitting in step-ten memory with a cold cache. Shepherd is a runtime layer by Stanford that records the run as a trace of typed events rather than a flat log. Each agent-environment interaction becomes a commit, similar to Git, but it tracks the live run. Its commit includes the agent process and the filesystem together, copy-on-write, so a branch carries the actual state and not just the files. Going back to a previous step is then a single call that forks from that commit and continues from the exact state. The copy-on-write fork is roughly five times faster than docker commit, and because the prompt prefix through step eight is unchanged, the KV cache is reused over 95% on replay, so early steps aren't reprocessed again. Once the run can be forked, a meta-agent can sit on top and operate it. It watches the trace and reverts as soon as it looks wrong, before the bad write is committed. In practice, it's just Python calling fork, replay, and revert on the trace, rather than a separate control plane wired into the harness. Not everything is reversible though. Files and sandbox changes undo themselves, but a database write has no automatic undo, so it needs a matching undo step set up in advance. Something external, like a sent email or a real charge, can't be undone, so the supervisor's job there is to catch it before it fires. They tested this on a few public benchmarks. On CooperBench, where two agents work on the same codebase, adding a live supervisor took the pair-coding pass rate from 28.8% to 54.7%. It's still early and labeled alpha. The benefit mostly shows up when a run gets branched a lot over a heavy sandbox state, which is exactly where restarting wastes the most tokens and time. If Git was made to make file changes reversible, Shepherd is trying to do the same thing for a live agent run. Shepherd Repo: (don't forget to star it ⭐ ) That said, Shepherd reverts a bad step inside a run. The harness around it, the prompts, tools, and checks the supervisor relies on, still drifts across runs as models and dependencies change. Akshay wrote about making that harness repair itself, where a failing trace gets diagnosed, the fix is verified against the exact input that failed, and the failure is locked as a regression test so it can't recur. Read it below.

Avi Chawla

442,276 Aufrufe • vor 3 Monaten