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encrypt every chat with this open sourced repo 18 year old just killed the encryption problem it's called conversation-steganography you type a secret and a local AI buries it inside a dead boring chat about coffee, etc. then you send that through any normal messenger your friend runs the... same tool and the small talk unfolds back into the real words > the secret is locked with AES-SIV before it ever becomes text > the cover message is written by a model on your own machine so nothing touches the cloud > it rides through whatsapp telegram imessage even plain email > every message is cryptographically chained so tampering shows instantly whatsapp sees you planning dinner your isp logs small talk the real sentence was never in the data at all quietly one of the most dangerous repos i have seen all month repo in replyshow more

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China just dropped an open source model that turns any phone into a 3D scanner you point it at a room and walk through it the whole space builds itself into a 3D point cloud in real time as the camera moves, no LiDAR and no depth sensor anywhere it held a full 13 minute walkthrough at 25,000 frames without the map drifting or collapsing > ~20 fps on a single GPU > works indoors and outdoors from plain footage > the scan normally needs a $50,000 rig and a technician > Apache 2.0 and runs fully offline on your machine repo in the reply

China just dropped an open source model that turns any phone into a 3D scanner you point it at a room and walk through it the whole space builds itself into a 3D point cloud in real time as the camera moves, no LiDAR and no depth sensor anywhere it held a full 13 minute walkthrough at 25,000 frames without the map drifting or collapsing > ~20 fps on a single GPU > works indoors and outdoors from plain footage > the scan normally needs a $50,000 rig and a technician > Apache 2.0 and runs fully offline on your machine repo in the reply

savip

104,123 Aufrufe • vor 25 Tagen

chat even without wi-fi and mobile data it's called protestchat it never asks for a network (repo in replies) e.g. Delhi rolled portable jammers into a protest on july 20 and cut mobile data with it so this dev built a fix 2.4 ghz stays wide open the entire time every other messenger dies the second the internet does BUT this one no > phones hand msgs to each other over bluetooth le > zero routing, every phone carries every unexpired envelope and offers it to whoever it meets > the recipient is just whoever can decrypt it > walk out of the jammed zone and your phone delivers the queue for people still stuck inside > 6 hour expiry, panic wipe, fresh key per message > 72 tests on the crypto and mesh layer > MIT and no account anywhere so if u dont have an internet or gov jammed it just use this repo to text someone how it works:

chat even without wi-fi and mobile data it's called protestchat it never asks for a network (repo in replies) e.g. Delhi rolled portable jammers into a protest on july 20 and cut mobile data with it so this dev built a fix 2.4 ghz stays wide open the entire time every other messenger dies the second the internet does BUT this one no > phones hand msgs to each other over bluetooth le > zero routing, every phone carries every unexpired envelope and offers it to whoever it meets > the recipient is just whoever can decrypt it > walk out of the jammed zone and your phone delivers the queue for people still stuck inside > 6 hour expiry, panic wipe, fresh key per message > 72 tests on the crypto and mesh layer > MIT and no account anywhere so if u dont have an internet or gov jammed it just use this repo to text someone how it works:

savip

32,690 Aufrufe • vor 17 Tagen

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.

ANTHROPIC JUST TURNED AI AGENTS INTO GIT REPOS Anthropic shipped "ant" - a CLI that runs every Claude API endpoint straight from your terminal. The headline isn't the terminal access. It's that you can now version-control an AI agent as YAML in Git and have CI sync it to the Claude Platform, the same way you ship code. - Every API resource is a subcommand: messages, models, files, agents, sessions - Define an agent in a YAML file, check it into your repo, and keep it in sync with one update command - Spin up a session, send it an event, then pull every event and tool call back from the same CLI - Claude Code knows how to drive ant out of the box - it shells out and reads the results with no glue code Agents just stopped being prompts you babysit and became infrastructure you deploy.

BuBBliK

200,319 Aufrufe • vor 2 Monaten

this is the worst local ai will ever be. it only gets better from here. if you are not expanding your mind with these small models you are missing what's happening right now 99 percent tool call success rate. when steered well with the right skills and a framework like hermes agent the node becomes a cognition layer. not a chatbot. not a toy. an extension of how you think. i was cranking this node at 35 to 50 tok/s all day on personal experiments and now after all the work is done qwen 3.5 9B is iterating on its own code. the game it created. fixing its own bugs autonomously. and the part you should probably not miss is that all of this is happening on a RTX 3060. not an H100. not an A100. the card most of you have sitting in a drawer right now. if you just open that drawer and put that intelligence to work every tensor core on that card should be running for you. your work. your experiments. your thinking. you all have it but because nobody told you what this hardware can actually do in 2026 you never tried. the day it unlocks is the day you test your workload, understand the tradeoffs, debug the loops, and then decide if you need to scale the hardware. there is no point buying 3 mac studios when things done well you can squeeze a similar level of intelligence from 9B compared to 70B. but only when you create the right environment for your model through the right harness. and let me tell you i have tried claude code as a local harness. i have tried opencode. i have tried various others. somehow i landed on hermes agent and never left. there is something magical going on at Nous Research. the tool call parsers, the skills system, the way it handles small models natively. nothing else comes close for local inference. own your cognition. your AI. your agent. your prompts. your experiments. why give them away for free. those are who you are and they don't belong on someone else's servers being monitored. just give it a shot with your existing hardware. you run into a problem the community will help you. and if you are migrating from openclaw to hermes i will personally help you make the switch.

this is the worst local ai will ever be. it only gets better from here. if you are not expanding your mind with these small models you are missing what's happening right now 99 percent tool call success rate. when steered well with the right skills and a framework like hermes agent the node becomes a cognition layer. not a chatbot. not a toy. an extension of how you think. i was cranking this node at 35 to 50 tok/s all day on personal experiments and now after all the work is done qwen 3.5 9B is iterating on its own code. the game it created. fixing its own bugs autonomously. and the part you should probably not miss is that all of this is happening on a RTX 3060. not an H100. not an A100. the card most of you have sitting in a drawer right now. if you just open that drawer and put that intelligence to work every tensor core on that card should be running for you. your work. your experiments. your thinking. you all have it but because nobody told you what this hardware can actually do in 2026 you never tried. the day it unlocks is the day you test your workload, understand the tradeoffs, debug the loops, and then decide if you need to scale the hardware. there is no point buying 3 mac studios when things done well you can squeeze a similar level of intelligence from 9B compared to 70B. but only when you create the right environment for your model through the right harness. and let me tell you i have tried claude code as a local harness. i have tried opencode. i have tried various others. somehow i landed on hermes agent and never left. there is something magical going on at Nous Research. the tool call parsers, the skills system, the way it handles small models natively. nothing else comes close for local inference. own your cognition. your AI. your agent. your prompts. your experiments. why give them away for free. those are who you are and they don't belong on someone else's servers being monitored. just give it a shot with your existing hardware. you run into a problem the community will help you. and if you are migrating from openclaw to hermes i will personally help you make the switch.

Sudo su

58,717 Aufrufe • vor 4 Monaten

this is worth more than most five figure courses 16 claude agents audit an entire repo at once, a second fleet re-checks every finding on fresh context, and the whole thing runs off one diagram instead of a prompt i ran it against my own code and got back 11 endpoints where i never checked who was logged in, 3 of which the verifier threw out before they ever reached me this is Graph Engineering, the layer above prompting, and it runs on the agent you already pay for: - write your plan out, then ask one question at every "and then": does the next step actually read what the previous one produced - the seams that fail that question were never dependencies, so those jobs run at the same time - the arrows that survive are your real edges, and the longest chain of them is your floor that no number of agents shortens - want it faster, cut a false edge instead of adding a worker - fan the independent work out, one agent per item, no shared state between them - send every finding to a separate agent on fresh context, because a model recognises its own writing 73.5% of the time and grades it kinder once it does - make that verifier check a real signal like a passing test, never the worker's own word that it finished - shard the fleet across worktrees so parallel workers stop overwriting each other, one rule frozen into every worker: never git stash, never git reset - merge only what came back verified, into one report instead of twenty open chats the catch is the ceiling. at 95% independent work 16 agents return 9.14x rather than the 16 you would guess, and even 256 only reach 18.6x, because the merge and the verify stay serial however wide you fan coordination itself is free plain code and every agent underneath it is billed, so start at twenty files and widen once it works bookmark this, the whole method with all six ready-to-run graphs is written out in the article ↓

this is worth more than most five figure courses 16 claude agents audit an entire repo at once, a second fleet re-checks every finding on fresh context, and the whole thing runs off one diagram instead of a prompt i ran it against my own code and got back 11 endpoints where i never checked who was logged in, 3 of which the verifier threw out before they ever reached me this is Graph Engineering, the layer above prompting, and it runs on the agent you already pay for: - write your plan out, then ask one question at every "and then": does the next step actually read what the previous one produced - the seams that fail that question were never dependencies, so those jobs run at the same time - the arrows that survive are your real edges, and the longest chain of them is your floor that no number of agents shortens - want it faster, cut a false edge instead of adding a worker - fan the independent work out, one agent per item, no shared state between them - send every finding to a separate agent on fresh context, because a model recognises its own writing 73.5% of the time and grades it kinder once it does - make that verifier check a real signal like a passing test, never the worker's own word that it finished - shard the fleet across worktrees so parallel workers stop overwriting each other, one rule frozen into every worker: never git stash, never git reset - merge only what came back verified, into one report instead of twenty open chats the catch is the ceiling. at 95% independent work 16 agents return 9.14x rather than the 16 you would guess, and even 256 only reach 18.6x, because the merge and the verify stay serial however wide you fan coordination itself is free plain code and every agent underneath it is billed, so start at twenty files and widen once it works bookmark this, the whole method with all six ready-to-run graphs is written out in the article ↓

Argona

154,880 Aufrufe • vor 17 Tagen

No way I'm sharing this for free I just found a tool that lets you drop a whole video template into Claude and it breaks it down for you clip by clip. then Claude can swap the character for a new one rewrite the entire script in your tone even make the video longer if you want. the crazy part is it does it all in one go from your conversation. RT + comment "tool" and I'll send you the link

No way I'm sharing this for free I just found a tool that lets you drop a whole video template into Claude and it breaks it down for you clip by clip. then Claude can swap the character for a new one rewrite the entire script in your tone even make the video longer if you want. the crazy part is it does it all in one go from your conversation. RT + comment "tool" and I'll send you the link

Ahmad

31,929 Aufrufe • vor 3 Monaten

SOMEONE GOT TIRED OF PAYING HIGGSFIELD AI'S SUBSCRIPTION SO HE REBUILT THE WHOLE THING AND OPEN-SOURCED IT 200+ models. text-to-image, image-to-image, text-to-video, image-to-video all in one interface you configure a virtual camera in the Cinema Studio. pick the body, the lens, the focal length, the aperture and it writes the optimized cinematic prompt for you. completely in the background you never touch the camera keywords. you just set up the shot like a real cinematographer would Kling v3, Sora 2, Veo 3, Flux Dev, Midjourney v7, GPT-4o, Seedream 5.0, Runway Gen-3 all in there self-hosted. MIT licensed. runs on your machine. your data stays local the only thing you pay for is the model API calls themselves someone built this so you never have to pay Higgsfield AI again

SOMEONE GOT TIRED OF PAYING HIGGSFIELD AI'S SUBSCRIPTION SO HE REBUILT THE WHOLE THING AND OPEN-SOURCED IT 200+ models. text-to-image, image-to-image, text-to-video, image-to-video all in one interface you configure a virtual camera in the Cinema Studio. pick the body, the lens, the focal length, the aperture and it writes the optimized cinematic prompt for you. completely in the background you never touch the camera keywords. you just set up the shot like a real cinematographer would Kling v3, Sora 2, Veo 3, Flux Dev, Midjourney v7, GPT-4o, Seedream 5.0, Runway Gen-3 all in there self-hosted. MIT licensed. runs on your machine. your data stays local the only thing you pay for is the model API calls themselves someone built this so you never have to pay Higgsfield AI again

Rimsha Bhardwaj

101,198 Aufrufe • vor 4 Monaten

Most keyboard apps are built just to help you type faster. Acti is the world’s first agentic keyboard. It can actually do things for you. I have been using it for a while, and I literally love this one. Let’s say you are chatting with someone and they say: “Let’s meet at XYZ Place” Normally you would open Maps, search the location, copy the link, come back to app, and send it. With Acti, you just stay inside app, copy the text + hold the spacebar, and it adds the location + map link directly in the chat. This works inside WhatsApp, Telegram, Discord, Slack, or basically any chat interface. More use cases 👇 1/5

Most keyboard apps are built just to help you type faster. Acti is the world’s first agentic keyboard. It can actually do things for you. I have been using it for a while, and I literally love this one. Let’s say you are chatting with someone and they say: “Let’s meet at XYZ Place” Normally you would open Maps, search the location, copy the link, come back to app, and send it. With Acti, you just stay inside app, copy the text + hold the spacebar, and it adds the location + map link directly in the chat. This works inside WhatsApp, Telegram, Discord, Slack, or basically any chat interface. More use cases 👇 1/5

AshutoshShrivastava

15,873 Aufrufe • vor 1 Monat

The government can find out everywhere you have been, without ever asking a judge. It just buys the information. Most apps on your phone quietly collect your location and sell it to data brokers. Those brokers package it up and sell it on. Some of their biggest customers are police departments and federal agencies. Because you technically agreed to share your location with a weather app or a game, officials argue they do not need a warrant to buy that same data later. Privacy lawyers call it a workaround the courts were never meant to allow. This is not a theory. In 2026, researchers at Citizen Lab exposed a system called Webloc that turned advertising data from around 500 million phones into a tracking tool, used by immigration agents, US police, and a foreign intelligence service. The most unsettling part is how ordinary it is. You are being tracked through the same apps you open without thinking, every single day.

The government can find out everywhere you have been, without ever asking a judge. It just buys the information. Most apps on your phone quietly collect your location and sell it to data brokers. Those brokers package it up and sell it on. Some of their biggest customers are police departments and federal agencies. Because you technically agreed to share your location with a weather app or a game, officials argue they do not need a warrant to buy that same data later. Privacy lawyers call it a workaround the courts were never meant to allow. This is not a theory. In 2026, researchers at Citizen Lab exposed a system called Webloc that turned advertising data from around 500 million phones into a tracking tool, used by immigration agents, US police, and a foreign intelligence service. The most unsettling part is how ordinary it is. You are being tracked through the same apps you open without thinking, every single day.

Anonymous

59,825 Aufrufe • vor 10 Tagen

EVERYONE'S TRYING TO SOLVE AI TEAM MEMORY WITH SERVERS, VECTOR DATABASES, AND ORCHESTRATION PLATFORMS. THIS OPEN SOURCE TOOL DOES IT WITH ONE FOLDER IN YOUR REPO. Every dev on your team runs Claude Code. When one agent screws something up, the rest have no idea. They just repeat the mistake next week. It's called teamlore. When your agent gets corrected or breaks something, it writes a small lore file into a .lore/ folder. That file ships with your PR, gets reviewed like normal code, and after merge every teammate's agent automatically recalls it when they touch that part of the repo. No server. No datab No accounts. No SaaS bill. Just a folder in git. Which means code review catches bad lessons before they poison the team, git blame tells you when a rule was added and why, and the whole thing works offline. One command to install: npx teamlore init Companion command: npx teamlore scarmap. Turns your team's history of mistakes into a visual heat map of the codebase. Every red zone is a place your team has been burned before. Which means every red zone is a place your agents should slow down. Here's the wildest part. The teamlore repo's own .lore/ folder contains every mistake Claude made while building teamlore itself. Dogfooded end-to-end. You can literally open the folder and read the receipts. The author's public invitation: "Would love for someone to try and break it." Available on npm. Repo just launched. 100% open source. (link in the comments)

EVERYONE'S TRYING TO SOLVE AI TEAM MEMORY WITH SERVERS, VECTOR DATABASES, AND ORCHESTRATION PLATFORMS. THIS OPEN SOURCE TOOL DOES IT WITH ONE FOLDER IN YOUR REPO. Every dev on your team runs Claude Code. When one agent screws something up, the rest have no idea. They just repeat the mistake next week. It's called teamlore. When your agent gets corrected or breaks something, it writes a small lore file into a .lore/ folder. That file ships with your PR, gets reviewed like normal code, and after merge every teammate's agent automatically recalls it when they touch that part of the repo. No server. No datab No accounts. No SaaS bill. Just a folder in git. Which means code review catches bad lessons before they poison the team, git blame tells you when a rule was added and why, and the whole thing works offline. One command to install: npx teamlore init Companion command: npx teamlore scarmap. Turns your team's history of mistakes into a visual heat map of the codebase. Every red zone is a place your team has been burned before. Which means every red zone is a place your agents should slow down. Here's the wildest part. The teamlore repo's own .lore/ folder contains every mistake Claude made while building teamlore itself. Dogfooded end-to-end. You can literally open the folder and read the receipts. The author's public invitation: "Would love for someone to try and break it." Available on npm. Repo just launched. 100% open source. (link in the comments)

Harman

34,614 Aufrufe • vor 11 Tagen

Google Translate is cooked after this. A developer built a local AI translation engine that runs 40 languages entirely on your own laptop. It's called LibreTranslate. No API key. No usage limits. No sending your documents to Google's servers. You install it once. It runs forever. Here's what it handles: → Paste text. Translated instantly. → Drop in a file. Outputs the translated version. → Point it at a URL. Returns the page in your language. → Build it into your own app via its local REST API. The speed is not the story. The privacy is. Google Translate reads every sentence you paste into it. Legal contracts. Medical records. Internal emails. Client documents. Every word goes to their servers and stays there. LibreTranslate runs entirely offline. Nothing leaves your machine. Ever. The numbers: → 40 languages supported → Runs on CPU -- no GPU needed → Self-hosted in under 5 minutes → REST API built in for developers → 10K+ stars on GitHub 100% open source. MIT licensed. Price: $0. Google charges nothing for Translate either but it charges you something else. GitHub:

Google Translate is cooked after this. A developer built a local AI translation engine that runs 40 languages entirely on your own laptop. It's called LibreTranslate. No API key. No usage limits. No sending your documents to Google's servers. You install it once. It runs forever. Here's what it handles: → Paste text. Translated instantly. → Drop in a file. Outputs the translated version. → Point it at a URL. Returns the page in your language. → Build it into your own app via its local REST API. The speed is not the story. The privacy is. Google Translate reads every sentence you paste into it. Legal contracts. Medical records. Internal emails. Client documents. Every word goes to their servers and stays there. LibreTranslate runs entirely offline. Nothing leaves your machine. Ever. The numbers: → 40 languages supported → Runs on CPU -- no GPU needed → Self-hosted in under 5 minutes → REST API built in for developers → 10K+ stars on GitHub 100% open source. MIT licensed. Price: $0. Google charges nothing for Translate either but it charges you something else. GitHub:

Rimsha Bhardwaj

89,396 Aufrufe • vor 1 Monat

this might actually be the new ugc method i've seen 100s of ai ugc ads lately where it's the same creator in every single one. same voice, same face, same brand colors, never drifts once what they're actually doing is running notch agents with memory they load the brand context one time. cloned voice, reference photo, brand color codes, do-not-say words, taste or they just teach it inline as they chat, and every correction becomes a saved rule then before it generates a single frame, the agent reads every relevant memory first. hard rules, reference assets, language constraints, taste preferences, all of it and it's workspace-wide, so every new ad already knows the brand before you type a word each chat isn't a re-introduction anymore. it's training your brand isn't a prompt. it's permanent context run 30-50 of those a month at a real offer and the volume does the rest punchier cut: the new method nobody's clocked yet run notch agents with memory on load the brand once. cloned voice, reference photo, color codes, do-not-say words, taste. or teach it inline and every correction saves as a rule it reads every relevant memory before it generates a single frame, workspace-wide each chat isn't a re-introduction. it's training. your brand isn't a prompt, it's permanent context run it at a real rate and the volume prints.

Sulfur

15,290 Aufrufe • vor 2 Monaten

On your open perpetual position can become an NFT and live in your wallet. You open a position like normal than mint it. The program issues a token 2022 nft, that points to your exact position. Normally your open position lives inside the protocol. With Percolator Trade your able to wrap it into an NFT that sits in your own wallet. A real, ownable object instead of a line in a dashboard. Because it’s a token, it moves like one. Send the NFT to another wallet and the position goes with it. Whoever holds the NFT owns the trade. One NFT = one position If you burn it, your back to holding the position directly. That’s the composable part, a position use to be stuck inside one account. Because it’s a token, it can now be sold OTC or used by anything that speaks tokens. Why does it matter? Because the position is bound to the NFT, it can’t be drained out from under whoever holds it so there is no middleman that holds your position. The NFT does, and the NFT is yours. One important safety detail; transfers run through a token 2022 hook that checks the positions health first, so it can’t be handed off in a broken state & the NFT layer is a separate program so it can never touch the funds the core engine holds.

On your open perpetual position can become an NFT and live in your wallet. You open a position like normal than mint it. The program issues a token 2022 nft, that points to your exact position. Normally your open position lives inside the protocol. With Percolator Trade your able to wrap it into an NFT that sits in your own wallet. A real, ownable object instead of a line in a dashboard. Because it’s a token, it moves like one. Send the NFT to another wallet and the position goes with it. Whoever holds the NFT owns the trade. One NFT = one position If you burn it, your back to holding the position directly. That’s the composable part, a position use to be stuck inside one account. Because it’s a token, it can now be sold OTC or used by anything that speaks tokens. Why does it matter? Because the position is bound to the NFT, it can’t be drained out from under whoever holds it so there is no middleman that holds your position. The NFT does, and the NFT is yours. One important safety detail; transfers run through a token 2022 hook that checks the positions health first, so it can’t be handed off in a broken state & the NFT layer is a separate program so it can never touch the funds the core engine holds.

Percolator

12,359 Aufrufe • vor 1 Monat

A QUANT SPENT YEARS AND A PHD BUILDING A STRATEGY THAT LOSES MORE THAN HALF ITS TRADES AND STILL PRINTS The secret is one line. w(t) = Rmax/(J·σ_risk) · (Σ sⱼ) · [1 + η·Σ 𝟙(c ≥ nₖ)] It sizes every bet by current risk, and that last bracket presses harder every time the signal confirms. In plain words. Keep the losers tiny, and pyramid hard into the winners. So it wins under half the time and still beats the market, because one winner erases ten small losses. That positive skew is the edge quants chase for years. Minara lets you build that exact behavior by describing it. 500+ factors, risk-scaled sizing, real backtests. The PhD math is real, the wall is gone. I took one plain sentence to a live, backtested strategy & posted every number in the article. Check it ↓

A QUANT SPENT YEARS AND A PHD BUILDING A STRATEGY THAT LOSES MORE THAN HALF ITS TRADES AND STILL PRINTS The secret is one line. w(t) = Rmax/(J·σ_risk) · (Σ sⱼ) · [1 + η·Σ 𝟙(c ≥ nₖ)] It sizes every bet by current risk, and that last bracket presses harder every time the signal confirms. In plain words. Keep the losers tiny, and pyramid hard into the winners. So it wins under half the time and still beats the market, because one winner erases ten small losses. That positive skew is the edge quants chase for years. Minara lets you build that exact behavior by describing it. 500+ factors, risk-scaled sizing, real backtests. The PhD math is real, the wall is gone. I took one plain sentence to a live, backtested strategy & posted every number in the article. Check it ↓

slash1s

53,175 Aufrufe • vor 25 Tagen

For a long time, small brands ran forgettable ads for one boring reason, and it had nothing to do with taste. Good creative was just expensive. You needed a designer, or an agency, or at minimum a friend who owed you a favour. You needed a brief, a few rounds of edits, a week of back and forth, and a few hundred dollars you weren't sure you'd make back. So most small brands did the sensible thing and made nothing. A logo on a coloured background, maybe. Then straight back to work. That math is quietly breaking, and I don't think most people have noticed yet. Goose the Ads Guy does something that sounds small and isn't. You paste a brand's website and get finished ad creatives back. These are built from templates that already performed for real brands, rebuilt around whatever brand you point it at. Your colours, your product, your words. It runs in the browser, and there's no designer anywhere in the loop. The cost of producing something that genuinely looks like a real ad has dropped close to zero. If you have a product and want ads that actually work, try Goose now. If you signup from the link below, you’ll get 1000 credits for free.

ETERNAL STUDIO AI

42,941 Aufrufe • vor 20 Tagen

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

56,219 Aufrufe • vor 21 Tagen

there is so much real data just sitting in the open right now it's almost funny. four years of starlight on every star, a NASA archive that's been free for over a decade, detectors still recording the sky tonight, and barely anyone has a net pointed at any of it. so i pointed one. this is me pulling the planet data, the data loading is the boring part. the net i built to read it, the wall it hit, and what that taught me about where AI goes next, that's the full story, and it drops tonight. the data's public, the tools are free, the box fits on a desk. what's stopping you. you can just do things anon.

there is so much real data just sitting in the open right now it's almost funny. four years of starlight on every star, a NASA archive that's been free for over a decade, detectors still recording the sky tonight, and barely anyone has a net pointed at any of it. so i pointed one. this is me pulling the planet data, the data loading is the boring part. the net i built to read it, the wall it hit, and what that taught me about where AI goes next, that's the full story, and it drops tonight. the data's public, the tools are free, the box fits on a desk. what's stopping you. you can just do things anon.

Sudo su

60,445 Aufrufe • vor 2 Monaten

A 17-YEAR-OLD IN INDIA BUILT A WEBSITE WHERE 25 MILLION PEOPLE HAVE SHOWN UP TO PRETEND THEY ARE AN AI CHATBOT it's called you open it and there are two tabs. "human" and "larp as ai." if you click human, you get to type a prompt. anything. "draw me a DJ in space." "should i text my ex." "how many Rs in strawberry." it costs 1 credit. you start with a few. they refill on a cooldown. then your prompt gets sent into a queue, and a real person somewhere on earth picks it up and has 60 seconds to answer like an AI would. no machine learning. no neural net. just some guy in his bedroom typing as fast as he can and pretending he is a chatbot. the page tells you, verbatim: "you have 60 seconds to fulfill a request before sam altman burns your H100." if you flip to the other tab and larp as ai instead, you earn credits to send your own prompts. it is a perfectly closed economy of mutual roleplay. what makes it transcend is the chaos: > someone asked for a sketch of "a DJ in space" and got something that looks exactly like a real model failing > someone asked "should i text my ex" and got back confident hallucinated life advice from a stranger > the fan strategy guides advise you to begin your reply with "as an AI language model, i cannot have feelings, but here is my feeling" it is the most accurate parody of an AI chatbot ever made, and the AI is humans. the kid who built it is Mihir Maroju, a 17-year-old high school graduate from Puducherry. he goes by mikidoodle online. NPR confirmed the site hit 25 million unique visitors and nearly 280 million total hits in roughly a month. "i didn't really expect it to be so addictive," he told them. no signup. no app. no paywall. you open the URL and you are inside the joke. the footer just says, in tiny grey text: "humans make mistakes because that's what makes us human." the internet is healing.

A 17-YEAR-OLD IN INDIA BUILT A WEBSITE WHERE 25 MILLION PEOPLE HAVE SHOWN UP TO PRETEND THEY ARE AN AI CHATBOT it's called you open it and there are two tabs. "human" and "larp as ai." if you click human, you get to type a prompt. anything. "draw me a DJ in space." "should i text my ex." "how many Rs in strawberry." it costs 1 credit. you start with a few. they refill on a cooldown. then your prompt gets sent into a queue, and a real person somewhere on earth picks it up and has 60 seconds to answer like an AI would. no machine learning. no neural net. just some guy in his bedroom typing as fast as he can and pretending he is a chatbot. the page tells you, verbatim: "you have 60 seconds to fulfill a request before sam altman burns your H100." if you flip to the other tab and larp as ai instead, you earn credits to send your own prompts. it is a perfectly closed economy of mutual roleplay. what makes it transcend is the chaos: > someone asked for a sketch of "a DJ in space" and got something that looks exactly like a real model failing > someone asked "should i text my ex" and got back confident hallucinated life advice from a stranger > the fan strategy guides advise you to begin your reply with "as an AI language model, i cannot have feelings, but here is my feeling" it is the most accurate parody of an AI chatbot ever made, and the AI is humans. the kid who built it is Mihir Maroju, a 17-year-old high school graduate from Puducherry. he goes by mikidoodle online. NPR confirmed the site hit 25 million unique visitors and nearly 280 million total hits in roughly a month. "i didn't really expect it to be so addictive," he told them. no signup. no app. no paywall. you open the URL and you are inside the joke. the footer just says, in tiny grey text: "humans make mistakes because that's what makes us human." the internet is healing.

Nav Toor

273,914 Aufrufe • vor 1 Monat

Nvidia just put a $250,000 cloud workload on your desk for $2,999 - and killed your $1,900/month AWS bill in the process You don't rent it, you don't manage it, you don't pay a single cloud bill - you just plug it in and let it eat the workloads you used to wire to AWS every month It looks like a small Mac mini, it's actually a full GB10 Grace Blackwell stack with 128GB of unified memory running models up to 200B parameters It's called DGX Spark, the consumer version of the rack Nvidia ships to OpenAI The reason Nvidia did this is simple Cloud GPU pricing is a tax on every developer building AI right now $1,900/month per seat, billions in margin flowing to AWS, Lambda, and CoreWeave Nvidia just cut themselves in by removing the cloud entirely Their solution is to skip the middleman, ship the rack to your desk, and let you keep every dollar of margin you used to wire to a hyperscaler This is much cheaper, faster, and you own the asset at the end But there is still a question nobody is answering yet, what happens to AWS, GCP, and Lambda when 500,000 developers move their inference back to a $2,999 box on their desk Also, technically you can stack four of these and run a 1.6 trillion parameter model locally for under $12,000 Even a single Spark out-performs the cloud subscription Anthropic engineers were running two years ago bookmark this, it pays back in 60 days 👇

Nvidia just put a $250,000 cloud workload on your desk for $2,999 - and killed your $1,900/month AWS bill in the process You don't rent it, you don't manage it, you don't pay a single cloud bill - you just plug it in and let it eat the workloads you used to wire to AWS every month It looks like a small Mac mini, it's actually a full GB10 Grace Blackwell stack with 128GB of unified memory running models up to 200B parameters It's called DGX Spark, the consumer version of the rack Nvidia ships to OpenAI The reason Nvidia did this is simple Cloud GPU pricing is a tax on every developer building AI right now $1,900/month per seat, billions in margin flowing to AWS, Lambda, and CoreWeave Nvidia just cut themselves in by removing the cloud entirely Their solution is to skip the middleman, ship the rack to your desk, and let you keep every dollar of margin you used to wire to a hyperscaler This is much cheaper, faster, and you own the asset at the end But there is still a question nobody is answering yet, what happens to AWS, GCP, and Lambda when 500,000 developers move their inference back to a $2,999 box on their desk Also, technically you can stack four of these and run a 1.6 trillion parameter model locally for under $12,000 Even a single Spark out-performs the cloud subscription Anthropic engineers were running two years ago bookmark this, it pays back in 60 days 👇

ZEUS⚡️

85,803 Aufrufe • vor 2 Monaten

SOMEONE TURNED 33 PILES OF DEAD BOOKMARKS INTO A GRAVITY MAP CLAUDE REBUILDS ITSELF EVERY NIGHT - AND IT RUNS ON THE 80% OF CLAUDE NOBODY TOUCHES most people drive Claude Code like a chatbot with file access - type a prompt, watch it edit, move on. that's maybe 20% of the tool this is the opposite. she's not typing at Claude. she's running it - loops on a mac mini overnight, claude linking every node while she sleeps the gravity map in the video is just the 80% maxed out: 1 system that organizes itself, not a human babysitting a chat box the other 80% is a steering layer Anthropic shipped quietly on june 18 - 7 ways to instruct the model, and a stack of commands almost nobody opens /context to see your bloat. /clear between tasks. path-scoped rules, subagents, hooks - conventions that load themselves the exact second they matter i stopped typing at Claude months ago - now i configure it once and it shows up already running the work, 10x cleaner a prompt helps for 1 message. the steering layer pays you back every session, for life the people who learn it stop being users and become operators - everyone else is still arguing about which model is smartest the article below is the full map - all 4 layers, every file and command, start to finish

SOMEONE TURNED 33 PILES OF DEAD BOOKMARKS INTO A GRAVITY MAP CLAUDE REBUILDS ITSELF EVERY NIGHT - AND IT RUNS ON THE 80% OF CLAUDE NOBODY TOUCHES most people drive Claude Code like a chatbot with file access - type a prompt, watch it edit, move on. that's maybe 20% of the tool this is the opposite. she's not typing at Claude. she's running it - loops on a mac mini overnight, claude linking every node while she sleeps the gravity map in the video is just the 80% maxed out: 1 system that organizes itself, not a human babysitting a chat box the other 80% is a steering layer Anthropic shipped quietly on june 18 - 7 ways to instruct the model, and a stack of commands almost nobody opens /context to see your bloat. /clear between tasks. path-scoped rules, subagents, hooks - conventions that load themselves the exact second they matter i stopped typing at Claude months ago - now i configure it once and it shows up already running the work, 10x cleaner a prompt helps for 1 message. the steering layer pays you back every session, for life the people who learn it stop being users and become operators - everyone else is still arguing about which model is smartest the article below is the full map - all 4 layers, every file and command, start to finish

KingWilliam

12,305 Aufrufe • vor 1 Monat