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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... show more

Virgile RIETSCH

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155,726 просмотров • 8 дней назад •via X (Twitter)

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save these two free services. they are what i built my Polymarket strategies on, the ones paying me around $10,000 a month. what it looks like day to day: you ask any question about the market in plain words, Claude Code digs through the data and comes back with numbers. you become a quant researcher. 1 - poly_data one command and every Polymarket trade sits on your own disk. who bought, when, at what price, what size, and who was standing on the other side of it. my first sync pulled 3.1 million markets. you can pull apart the edge of any trader on Polymarket you like. first sync runs a few hours, leave it overnight, after that updates land in seconds. 2 - pendulumflow archive this one holds the book itself: what orders were sitting there, at what prices, what size, second by second, on every single market. 288 billion events recorded, and 2.3 billion more coming in every day. i used to pay $200 a month for this kind of data. here you just open it and take it, nothing to sign up for. the front page has ready made questions you copy straight into Claude Code. the project runs on donations, storing and serving this much data costs real money every month. so if you start using it, send the guy something, he is carrying all of it alone. these two will give you what only a handful of people on this market have.

save these two free services. they are what i built my Polymarket strategies on, the ones paying me around $10,000 a month. what it looks like day to day: you ask any question about the market in plain words, Claude Code digs through the data and comes back with numbers. you become a quant researcher. 1 - poly_data one command and every Polymarket trade sits on your own disk. who bought, when, at what price, what size, and who was standing on the other side of it. my first sync pulled 3.1 million markets. you can pull apart the edge of any trader on Polymarket you like. first sync runs a few hours, leave it overnight, after that updates land in seconds. 2 - pendulumflow archive this one holds the book itself: what orders were sitting there, at what prices, what size, second by second, on every single market. 288 billion events recorded, and 2.3 billion more coming in every day. i used to pay $200 a month for this kind of data. here you just open it and take it, nothing to sign up for. the front page has ready made questions you copy straight into Claude Code. the project runs on donations, storing and serving this much data costs real money every month. so if you start using it, send the guy something, he is carrying all of it alone. these two will give you what only a handful of people on this market have.

may.crypto {🦅}

26,174 просмотров • 1 месяц назад

$We built a tool that finds everything the internet knows about you. Most people think they're careful online. They're not. Here's what the agents pull within minutes: • Old passwords exposed in breaches you forgot about. • Usernames from accounts you abandoned • Every platform you've ever signed up for. • Your full address history. • Family members surfaced from public records. • Photos tied to your name on sites you never posted to. That's a fraction of what comes back. In 2026 anyone with the right tool finds this in minutes. The least you can do is see it on yourself first so you can take it down. Free to run on yourself. Link in comments.$

We built a tool that finds everything the internet knows about you. Most people think they're careful online. They're not. Here's what the agents pull within minutes: • Old passwords exposed in breaches you forgot about. • Usernames from accounts you abandoned • Every platform you've ever signed up for. • Your full address history. • Family members surfaced from public records. • Photos tied to your name on sites you never posted to. That's a fraction of what comes back. In 2026 anyone with the right tool finds this in minutes. The least you can do is see it on yourself first so you can take it down. Free to run on yourself. Link in comments.

Saarth Shah

382,246 просмотров • 5 месяцев назад

I can't believe this is real I have GLM 5.2 running 100% locally on my Mac Studio. 2 bit quant. The results I'm getting are better than Opus 4.8 It's now powering my Hermes Agent and Codex. 100% free, local, private super intelligence on my desk I also have it in a loop coding for me 24/7 now I thought we were at least a year away from this type of event. It happened today. The model takes up about 250gb of memory. So you can technically run it on a Mac Studio with 256gb, but you probably want the 512gb memory version (please tell me you listened to me 5 months ago when these were sitting on store shelves) With Fable gone, I now have Opus 4.8 level intelligence on my desk for free. This is the future. Local, private, secure, personal super intelligence. If you're still writing off local AI as a fad or engagement bait, you are officially delusional

I can't believe this is real I have GLM 5.2 running 100% locally on my Mac Studio. 2 bit quant. The results I'm getting are better than Opus 4.8 It's now powering my Hermes Agent and Codex. 100% free, local, private super intelligence on my desk I also have it in a loop coding for me 24/7 now I thought we were at least a year away from this type of event. It happened today. The model takes up about 250gb of memory. So you can technically run it on a Mac Studio with 256gb, but you probably want the 512gb memory version (please tell me you listened to me 5 months ago when these were sitting on store shelves) With Fable gone, I now have Opus 4.8 level intelligence on my desk for free. This is the future. Local, private, secure, personal super intelligence. If you're still writing off local AI as a fad or engagement bait, you are officially delusional

Alex Finn

624,407 просмотров • 3 месяцев назад

🤯 THIS FEELS ILLEGAL AND I LOVE IT. I found a personal AI assistant that actually runs on your computer 24/7 and gets smarter the more you use it. It is called Mercury. 100% FREE. Think of it like having a smart assistant who never forgets anything. Here is what it does: ↳ Remembers everything about you ↳ Asks before doing anything risky It will not run commands or touch your files without permission. You stay in control always. ↳ You can message it from anywhere ↳ Has 31 tools built in ↳ Protects your AI credits One command to install: npx @cosmicstack/mercury-agent 100% OpenSource. Works on Mac, Windows, and Linux.

🤯 THIS FEELS ILLEGAL AND I LOVE IT. I found a personal AI assistant that actually runs on your computer 24/7 and gets smarter the more you use it. It is called Mercury. 100% FREE. Think of it like having a smart assistant who never forgets anything. Here is what it does: ↳ Remembers everything about you ↳ Asks before doing anything risky It will not run commands or touch your files without permission. You stay in control always. ↳ You can message it from anywhere ↳ Has 31 tools built in ↳ Protects your AI credits One command to install: npx @cosmicstack/mercury-agent 100% OpenSource. Works on Mac, Windows, and Linux.

Kanika

13,601 просмотров • 4 месяцев назад

the opportunity's AI UGC opened up for affiliate is absolutely insane I built an AI UGC system to promote sweeps offers on Glitchy just using Arcads + Claude Cowork Claude literally: > Scans TikTok / Reddit based on what I'm promoting > Generates me 50 hooks using this information > Creates me a full script based on the hooks > Uses the Arcads API to decide what tools to use for the script and generates the video > Then will analyse what hooks did well and which didn't and change the hook or CTA based on that information I can literally sleep while it cooks me up a weeks worth of content I made a full guide on how to set Claude Cowork up to generate videos with Arcads if you want it comment "UGC" and i'll send it to you (Must be following so i can DM you)

the opportunity's AI UGC opened up for affiliate is absolutely insane I built an AI UGC system to promote sweeps offers on Glitchy just using Arcads + Claude Cowork Claude literally: > Scans TikTok / Reddit based on what I'm promoting > Generates me 50 hooks using this information > Creates me a full script based on the hooks > Uses the Arcads API to decide what tools to use for the script and generates the video > Then will analyse what hooks did well and which didn't and change the hook or CTA based on that information I can literally sleep while it cooks me up a weeks worth of content I made a full guide on how to set Claude Cowork up to generate videos with Arcads if you want it comment "UGC" and i'll send it to you (Must be following so i can DM you)

Pounds

17,304 просмотров • 6 месяцев назад

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:

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

150,680 просмотров • 2 месяцев назад

What goes into a perfect offensive possession? For me, I evaluate it with something called the "Basketball Bliss Point. I wrote about what that is and how you can use it to build offensive concepts at The Painted Possession. I made this one FREE for everyone to read, so I hope you check it out. Link is in the first reply 👇. Before you read, there is some required viewing--my favorite offensive possession in Europe last year:

What goes into a perfect offensive possession? For me, I evaluate it with something called the "Basketball Bliss Point. I wrote about what that is and how you can use it to build offensive concepts at The Painted Possession. I made this one FREE for everyone to read, so I hope you check it out. Link is in the first reply 👇. Before you read, there is some required viewing--my favorite offensive possession in Europe last year:

Eric Fawcett

17,051 просмотров • 28 дней назад

1 Neural Network + Obsidian + Karpathy’s 1-file method = the most unhinged second brain build of 2026. It remembers everything you’ve ever done, and it costs $0 on top of what you already pay. The base is Karpathy’s append and review: 1 giant note, new thoughts stack on top, old ones sink, every few days you reread and pull the survivors back up. No folders, no tags, no plugins the rereading IS the system, because review is what turns storage into thinking. The flaw: past 10,000 lines, no human rereads anything. That’s where the neural network takes over. You keep the note in Obsidian 1 vault, everything dumps to the top: ideas, links, meeting fragments, half-thoughts. You never organize, you only dump. It all lives as plain markdown on your own disk, and that detail is the whole trick. Because now you point Claude Code at the vault folder, and it reads every line you’ve ever written. “What did I think about pricing in March.” “Find the 3 ideas I keep circling.” “What did I drop that deserves a second look.” It answers from YOUR notes, with quotes, in 15 seconds. Then once a week, 1 prompt closes the loop: read the last 7 days, surface the 5 entries worth pulling back up, flag anything that contradicts what I wrote a month ago. The model does the sinking and surfacing Karpathy did by hand, and the note stays alive instead of turning into a graveyard. Week 1 feels like nothing. Week 4 you hit the first “I already solved this in January.” Month 3 you consult your past self more than Google. Most second brains die in 11 days under 40 plugins and 200 folders. This one is 1 file and a loop, and it compounds because dumping takes 0 discipline. Notion stores what you thought. This thing argues back.

1 Neural Network + Obsidian + Karpathy’s 1-file method = the most unhinged second brain build of 2026. It remembers everything you’ve ever done, and it costs $0 on top of what you already pay. The base is Karpathy’s append and review: 1 giant note, new thoughts stack on top, old ones sink, every few days you reread and pull the survivors back up. No folders, no tags, no plugins the rereading IS the system, because review is what turns storage into thinking. The flaw: past 10,000 lines, no human rereads anything. That’s where the neural network takes over. You keep the note in Obsidian 1 vault, everything dumps to the top: ideas, links, meeting fragments, half-thoughts. You never organize, you only dump. It all lives as plain markdown on your own disk, and that detail is the whole trick. Because now you point Claude Code at the vault folder, and it reads every line you’ve ever written. “What did I think about pricing in March.” “Find the 3 ideas I keep circling.” “What did I drop that deserves a second look.” It answers from YOUR notes, with quotes, in 15 seconds. Then once a week, 1 prompt closes the loop: read the last 7 days, surface the 5 entries worth pulling back up, flag anything that contradicts what I wrote a month ago. The model does the sinking and surfacing Karpathy did by hand, and the note stays alive instead of turning into a graveyard. Week 1 feels like nothing. Week 4 you hit the first “I already solved this in January.” Month 3 you consult your past self more than Google. Most second brains die in 11 days under 40 plugins and 200 folders. This one is 1 file and a loop, and it compounds because dumping takes 0 discipline. Notion stores what you thought. This thing argues back.

West Lord

24,679 просмотров • 3 месяцев назад

a clip of a stream did MILLIONS of views last week. the guy who cut it got a like the moment was already proven. someone just had to find it before it cooled off - and finding it is the slow part everyone does by hand so i built ClipVein. open source, MIT it doesn't scrape and it doesn't post for you. it attaches to your own chrome and reads the same For You feed you already scroll what it does, all in the repo in plain text: → pulls every streamer post with its real metrics -views, likes, reposts, video → keeps only clips that clear a view floor YOU set → ranks them on reach + engagement + freshness, and shows you WHY each one scored → hands you the top 3 links, ready to re-cut and repost for original views the honest parts are the point: it uses your session, so it never asks for a password it can't post for you. no auto-post, ever runs on your machine, not mine. MIT, tests green optional: grok scouts the feed, claude writes the caption repo in the first reply

a clip of a stream did MILLIONS of views last week. the guy who cut it got a like the moment was already proven. someone just had to find it before it cooled off - and finding it is the slow part everyone does by hand so i built ClipVein. open source, MIT it doesn't scrape and it doesn't post for you. it attaches to your own chrome and reads the same For You feed you already scroll what it does, all in the repo in plain text: → pulls every streamer post with its real metrics -views, likes, reposts, video → keeps only clips that clear a view floor YOU set → ranks them on reach + engagement + freshness, and shows you WHY each one scored → hands you the top 3 links, ready to re-cut and repost for original views the honest parts are the point: it uses your session, so it never asks for a password it can't post for you. no auto-post, ever runs on your machine, not mine. MIT, tests green optional: grok scouts the feed, claude writes the caption repo in the first reply

Groven

32,567 просмотров • 1 месяц назад

1.7 billion free tokens per month. A month ago i showed you how to route claude code through free providers. someone just shipped the cleanest version of this setup yet… it's called Freellmapi 13,400+ stars on github, MIT licensed, takes 2 minutes to install. what it does: stacks the free tiers of 16 different LLM providers behind one local API. point claude code, codex, or cursor at that one endpoint, and it automatically routes your calls across all 16 free pools. The 16 providers it covers: Google, Groq, Cerebras, Mistral, OpenRouter, GitHub Models, Cloudflare, Cohere, NVIDIA, HuggingFace, Ollama Cloud, Kilo, Pollinations, LLM7, OVH, and OpenCode Zen. if you sign up to all 16 and add your free API keys, you get roughly 1.7 billion free tokens per month combined. ▫️ How to install (one command) curl -fsSL bash this runs the whole thing locally on your machine through Docker. once it's up, open paste your provider keys on the Keys page, and grab the unified API key from the dashboard. that's the key you point your apps at. With this, claude code stops hitting your monthly cap because every prompt routes through the 16 free pools instead of your paid plan. and if one provider rate-limits mid-conversation, freellmapi falls over to the next one automatically so your session never breaks. repo: Free, MIT-licensed, runs on your laptop or a $5 VPS.

1.7 billion free tokens per month. A month ago i showed you how to route claude code through free providers. someone just shipped the cleanest version of this setup yet… it's called Freellmapi 13,400+ stars on github, MIT licensed, takes 2 minutes to install. what it does: stacks the free tiers of 16 different LLM providers behind one local API. point claude code, codex, or cursor at that one endpoint, and it automatically routes your calls across all 16 free pools. The 16 providers it covers: Google, Groq, Cerebras, Mistral, OpenRouter, GitHub Models, Cloudflare, Cohere, NVIDIA, HuggingFace, Ollama Cloud, Kilo, Pollinations, LLM7, OVH, and OpenCode Zen. if you sign up to all 16 and add your free API keys, you get roughly 1.7 billion free tokens per month combined. ▫️ How to install (one command) curl -fsSL bash this runs the whole thing locally on your machine through Docker. once it's up, open paste your provider keys on the Keys page, and grab the unified API key from the dashboard. that's the key you point your apps at. With this, claude code stops hitting your monthly cap because every prompt routes through the 16 free pools instead of your paid plan. and if one provider rate-limits mid-conversation, freellmapi falls over to the next one automatically so your session never breaks. repo: Free, MIT-licensed, runs on your laptop or a $5 VPS.

Axel Bitblaze 🪓

48,825 просмотров • 3 месяцев назад

if you use claude code, this will save you real money. the problem: every time you make an edit, your ai rereads the whole codebase to figure out what changed. tens of thousands of tokens, every turn, for context it already had. this repo fixes it. it’s called code review graph, and it just maps your entire codebase: every file, every function, every connection laid out so you can see the actual shape of your project how to set up (2 min): 1. pip install code-review-graph 2. code-review-graph install - auto-configures Claude Code, Cursor, Codex, Gemini CLI + more 3. code-review-graph build change one function, and it traces exactly what that touches... every caller, every dependent file, every test. your ai only reads what's affected, not the whole repo. and the savings are wild. a task that used to burn ~100,000 tokens (about a dollar) now runs closer to a penny.

if you use claude code, this will save you real money. the problem: every time you make an edit, your ai rereads the whole codebase to figure out what changed. tens of thousands of tokens, every turn, for context it already had. this repo fixes it. it’s called code review graph, and it just maps your entire codebase: every file, every function, every connection laid out so you can see the actual shape of your project how to set up (2 min): 1. pip install code-review-graph 2. code-review-graph install - auto-configures Claude Code, Cursor, Codex, Gemini CLI + more 3. code-review-graph build change one function, and it traces exactly what that touches... every caller, every dependent file, every test. your ai only reads what's affected, not the whole repo. and the savings are wild. a task that used to burn ~100,000 tokens (about a dollar) now runs closer to a penny.

Alvaro Cintas

75,596 просмотров • 2 месяцев назад

Just launched today 🤖🐘 Postgres intelligence for agents & apps. Free. Open source. Written in Go. Nobody wants to stare at PostgreSQL dashboards all day. So I built a bot that does it for you. → finds what’s wrong → explains what changed → tells you what to fix first Metrics are data. pgbot gives you answers pgbot can: +PostgreSQL health monitoring +Query performance analysis +Slow query and regression detection +Locks and blocking query insights +Table and index analytics +Vacuum and autovacuum visibility +Database growth tracking +AI root-cause analysis +AI-powered recommendations +Direct connection or agent-based private access

Just launched today 🤖🐘 Postgres intelligence for agents & apps. Free. Open source. Written in Go. Nobody wants to stare at PostgreSQL dashboards all day. So I built a bot that does it for you. → finds what’s wrong → explains what changed → tells you what to fix first Metrics are data. pgbot gives you answers pgbot can: +PostgreSQL health monitoring +Query performance analysis +Slow query and regression detection +Locks and blocking query insights +Table and index analytics +Vacuum and autovacuum visibility +Database growth tracking +AI root-cause analysis +AI-powered recommendations +Direct connection or agent-based private access

alex shapalov

98,554 просмотров • 2 месяцев назад

Stop manually making flowcharts. Try this GitHub skill with Claude Code. I found an open-source tool that turns a plain description of a software system into a clean, interactive diagram. It's called Archify, and it has more than 66,000 stars on GitHub. You add it as a skill to an AI coding agent like Claude Code, Codex, Cursor, or OpenCode. Then you describe the system in the chat in one line. Archify turns that line into a single HTML file you can open, click through, and send to anyone. If you point your agent at an existing codebase, it reads the code and Archify maps how the pieces connect. I liked five things about it. 1. It makes five kinds of diagrams, including system maps and step-by-step workflows. 2. You can click any piece and trace what connects to it. 3. It checks the layout before it hands the diagram over, so arrows don't pile up and labels don't overlap. 4. It has dark and light themes and exports to PNG, SVG, or a short video. 5. You keep editing in plain chat, with requests like "add Redis" or "highlight the rollback path." You don't need a codebase to use it. A description in the chat is enough.

Stop manually making flowcharts. Try this GitHub skill with Claude Code. I found an open-source tool that turns a plain description of a software system into a clean, interactive diagram. It's called Archify, and it has more than 66,000 stars on GitHub. You add it as a skill to an AI coding agent like Claude Code, Codex, Cursor, or OpenCode. Then you describe the system in the chat in one line. Archify turns that line into a single HTML file you can open, click through, and send to anyone. If you point your agent at an existing codebase, it reads the code and Archify maps how the pieces connect. I liked five things about it. 1. It makes five kinds of diagrams, including system maps and step-by-step workflows. 2. You can click any piece and trace what connects to it. 3. It checks the layout before it hands the diagram over, so arrows don't pile up and labels don't overlap. 4. It has dark and light themes and exports to PNG, SVG, or a short video. 5. You keep editing in plain chat, with requests like "add Redis" or "highlight the rollback path." You don't need a codebase to use it. A description in the chat is enough.

Alex Veremeyenko

61,928 просмотров • 25 дней назад

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

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

18,257 просмотров • 1 месяц назад

New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.

New open-source agent harness just landed! I got early access to TrueForge by TrueFoundry and have been running it locally for the past few days. The harness layer deserves as much attention as the model, and open source matters here because you can inspect the loop, run it on your own infrastructure, and swap to the latest or cheaper models. TrueForge handles the runtime work that makes an agent reliable. It drives the tool-calling loop, manages context, coordinates subagents, and executes code in a sandbox, with any model you choose. Every tool call re-sends the growing context to the model, so in practice the harness controls most of what an agent costs to run. A few things stood out from my testing and their published benchmarks. Vendor-Neutral by design. It runs OpenAI, Anthropic, and Google models alongside open-weight models like Kimi, GLM, and DeepSeek. Model routing is a setting, and you can send each task to the model that fits it. On a 14-task enterprise agent benchmark, it matched the accuracy of Claude Managed Agents running the same Opus 4.8 model at roughly 30% lower cost per run (3.8M tokens vs 10M for the same answers). Routing the same tasks to GLM-5.2 held accuracy and brought cost down by about 75%, around $3 per run instead of $12. Fully self-hosted and Open Source (MIT License). I had it running locally with one command, with sandboxed code execution working out of the box. It's time to own your agent harness. Thanks to TrueFoundry for partnering on this post.

elvis

11,303 просмотров • 1 месяц назад

“Polymarket dev accidentally uploaded a wife-changing money-printer bot and I managed to snatch the code.” My timeline is full of this AI Polymarket slop, all running the same recycled video, lying to you that they have some magical, working code. Most of you have never seen what a real, working terminal actually looks like. Let me tell you this: it doesn’t look pretty, it doesn’t look like that, and it definitely doesn’t have source code scrolling down the screen like an ad. Second ‘be careful out there’ post for today. Drop a follow if you want quality, scam-free posts on your timeline.

“Polymarket dev accidentally uploaded a wife-changing money-printer bot and I managed to snatch the code.” My timeline is full of this AI Polymarket slop, all running the same recycled video, lying to you that they have some magical, working code. Most of you have never seen what a real, working terminal actually looks like. Let me tell you this: it doesn’t look pretty, it doesn’t look like that, and it definitely doesn’t have source code scrolling down the screen like an ad. Second ‘be careful out there’ post for today. Drop a follow if you want quality, scam-free posts on your timeline.

Jayden ⛩️

127,197 просмотров • 8 месяцев назад

i quit my “dream” web3 job last month. remote. good pay. chill life. from the outside, it looked perfect. on the inside, i was stuck in the same 9–5 loop, waiting for a paycheck instead of doing what i actually love. i’m 22. people say “you can take risks at any age” ~ but for me, it feels like now or never. this is only the second video i’ve ever made. the audio might be mid, the edit might be rough, but it’s real. here’s the video: watch it and roast / review / share what i can improve.

i quit my “dream” web3 job last month. remote. good pay. chill life. from the outside, it looked perfect. on the inside, i was stuck in the same 9–5 loop, waiting for a paycheck instead of doing what i actually love. i’m 22. people say “you can take risks at any age” ~ but for me, it feels like now or never. this is only the second video i’ve ever made. the audio might be mid, the edit might be rough, but it’s real. here’s the video: watch it and roast / review / share what i can improve.

pawan

15,743 просмотров • 8 месяцев назад

2 weeks ago i started delegating everything to an AI agent not just writing. actual operations. here's exactly what it does for me now: 1. trades polymarket 24/7 (378 wins, 27 losses, 93% win rate) 2. sends me a morning brief every day at 7:30am with personalized tweet suggestions based on what performed yesterday 3. scrapes my own analytics and tracks what's working 4. monitors all my bots and alerts me when something breaks 5. checks my email and calendar so i don't have to total setup cost: $0. it runs on a $600 mac mini. the part nobody tells you: the first week was terrible. bad suggestions, wrong data, generic output. but every time it got something wrong, i told it why. and it got better. AI isn't plug and play. it's a team member you train with honest feedback. the people treating AI like a google search will never get here.

2 weeks ago i started delegating everything to an AI agent not just writing. actual operations. here's exactly what it does for me now: 1. trades polymarket 24/7 (378 wins, 27 losses, 93% win rate) 2. sends me a morning brief every day at 7:30am with personalized tweet suggestions based on what performed yesterday 3. scrapes my own analytics and tracks what's working 4. monitors all my bots and alerts me when something breaks 5. checks my email and calendar so i don't have to total setup cost: $0. it runs on a $600 mac mini. the part nobody tells you: the first week was terrible. bad suggestions, wrong data, generic output. but every time it got something wrong, i told it why. and it got better. AI isn't plug and play. it's a team member you train with honest feedback. the people treating AI like a google search will never get here.

Sharbel

16,692 просмотров • 8 месяцев назад