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Storybook Test sneak peek 👀 ⚡️ Blazing fast tests that run in the browser 🌈 Component, visual, and a11y tests simultaneously 🚦 Visualize results inside Storybook 👉 Click-to-debug in your own browser 🌐 Optimized for test coverage 💻 VSCode integration via Vitest

Storybook

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Introducing the Stripe.js testing assistant! Integrate, test, and debug Stripe Elements directly on your website, right in your browser. 🪄 Autofill Stripe Elements to test payment methods and scenarios 🌐 Simulate checkout for your customers and see payment method availability by locale ⚠️ Catch integration errors and warnings as you set up

Introducing the Stripe.js testing assistant! Integrate, test, and debug Stripe Elements directly on your website, right in your browser. 🪄 Autofill Stripe Elements to test payment methods and scenarios 🌐 Simulate checkout for your customers and see payment method availability by locale ⚠️ Catch integration errors and warnings as you set up

Michael Luo

10,789 views • 7 months ago

Vorflux has opened its new cloud platform, allowing users to run tasks from a plan to a merged PR on a dedicated machine. It can plan, build, test, and review on its own. > Diff reviews are done by a different model family than the one that wrote it by default. > First boot is saved as a snapshot, with all subsequent sessions waking up from that same starting point. > A browser agent walks through the real user flow, and the recording is included in the pull request. Video proof in the PR 👀

Vorflux has opened its new cloud platform, allowing users to run tasks from a plan to a merged PR on a dedicated machine. It can plan, build, test, and review on its own. > Diff reviews are done by a different model family than the one that wrote it by default. > First boot is saved as a snapshot, with all subsequent sessions waking up from that same starting point. > A browser agent walks through the real user flow, and the recording is included in the pull request. Video proof in the PR 👀

🚨 AI News | TestingCatalog

11,385 views • 7 days ago

🌍 As some of you might know, last year we started building an app that required a 3D Map, and we were taken aback by the lack of good SDKs. They’re all clunky, slow, and unstable.🤔 Today, we're thrilled to introduce Cartes - a fast, easy-to-use, and visually appealing 3D Map SDK for Unity. This tool is built to empower your creativity in developing delightful apps and games for the Real World Metaverse. 📱🤳 ⚡️ With blazing-fast performance, our SDK offers a seamless integration for your (modern) Unity projects. We provide default navigation features, intuitive gestures, and clustering capabilities that we meticulously refined over hundreds of hours and proof-tested in guerilla tests. 🏞️ We wanted to build a genuinely 3D map, with 3D terrain, monuments and decorations, able to transition smoothly from a global view down to the human eye level. 🔍 #Unity #AR #Maps #LBE

🌍 As some of you might know, last year we started building an app that required a 3D Map, and we were taken aback by the lack of good SDKs. They’re all clunky, slow, and unstable.🤔 Today, we're thrilled to introduce Cartes - a fast, easy-to-use, and visually appealing 3D Map SDK for Unity. This tool is built to empower your creativity in developing delightful apps and games for the Real World Metaverse. 📱🤳 ⚡️ With blazing-fast performance, our SDK offers a seamless integration for your (modern) Unity projects. We provide default navigation features, intuitive gestures, and clustering capabilities that we meticulously refined over hundreds of hours and proof-tested in guerilla tests. 🏞️ We wanted to build a genuinely 3D map, with 3D terrain, monuments and decorations, able to transition smoothly from a global view down to the human eye level. 🔍 #Unity #AR #Maps #LBE

Tina Debove ᯅ

15,210 views • 3 years ago

CHINA JUST LEAKED THE FUTURE OF WEB APPS. Alibaba open-sourced PageAgent and 99% of SaaS founders are sleeping on this. It's a JavaScript AI agent that lives INSIDE your webpage. Users control your entire interface with natural language. ↳ No browser extensions needed, screenshots or multi-modal LLMs, headless browser setup, and also no backend rewrite required Just drop it in your HTML with ONE line of code. What took 20 clicks now takes one sentence. "Click login, fill in my credentials, submit the form" Done. This is not a demo, it is production-ready. ↳ Turn any SaaS into an AI copilot in minutes ↳ Smart form filling for ERP, CRM, admin systems ↳ Voice commands and accessibility built in ↳ Multi-page agent tasks via Chrome extension ↳ MCP server support for external control ↳ Bring your own LLM (Qwen, GPT, Claude, anything) Every founder building AI features just got a shortcut. Every developer manually building copilots just got replaced. The integration looks like this: That's it. Your app now has an AI agent.

CHINA JUST LEAKED THE FUTURE OF WEB APPS. Alibaba open-sourced PageAgent and 99% of SaaS founders are sleeping on this. It's a JavaScript AI agent that lives INSIDE your webpage. Users control your entire interface with natural language. ↳ No browser extensions needed, screenshots or multi-modal LLMs, headless browser setup, and also no backend rewrite required Just drop it in your HTML with ONE line of code. What took 20 clicks now takes one sentence. "Click login, fill in my credentials, submit the form" Done. This is not a demo, it is production-ready. ↳ Turn any SaaS into an AI copilot in minutes ↳ Smart form filling for ERP, CRM, admin systems ↳ Voice commands and accessibility built in ↳ Multi-page agent tasks via Chrome extension ↳ MCP server support for external control ↳ Bring your own LLM (Qwen, GPT, Claude, anything) Every founder building AI features just got a shortcut. Every developer manually building copilots just got replaced. The integration looks like this: That's it. Your app now has an AI agent.

Kanika

332,229 views • 1 month ago

i just open sourced the workflow behind $2M AI video productions... i built 7 skills that run the pipeline end to end, built for Seedance 2.5 and they work in Claude Code, Codex, Hermes or any harness (works best with 1080p using Higgsfield CLI) here's how to use them, in order: /setup writes which image and video models you run into your project, once, so every skill reads the same stack /studio-init scaffolds the whole studio as a file tree from one question, the project name /film-breakdown walks your script scene by scene and writes a 22-field card for every shot /reference-board locks your references into a visual bible, a caption on every image and a ban list for the rest /asset-passport writes the exhaustive descriptor every later prompt will quote word for word /stress-test combat-tests each asset and flips it to locked only at 10 out of 10 repeatability /shot-prompt refuses to run until everything in frame is locked, then writes the 15-block prompt and logs every attempt get access to the skills and full breakdown of the pipeline in the article below:

i just open sourced the workflow behind $2M AI video productions... i built 7 skills that run the pipeline end to end, built for Seedance 2.5 and they work in Claude Code, Codex, Hermes or any harness (works best with 1080p using Higgsfield CLI) here's how to use them, in order: /setup writes which image and video models you run into your project, once, so every skill reads the same stack /studio-init scaffolds the whole studio as a file tree from one question, the project name /film-breakdown walks your script scene by scene and writes a 22-field card for every shot /reference-board locks your references into a visual bible, a caption on every image and a ban list for the rest /asset-passport writes the exhaustive descriptor every later prompt will quote word for word /stress-test combat-tests each asset and flips it to locked only at 10 out of 10 repeatability /shot-prompt refuses to run until everything in frame is locked, then writes the 15-block prompt and logs every attempt get access to the skills and full breakdown of the pipeline in the article below:

Machina

59,261 views • 8 days ago

You can now post-train a model inside your existing production harness with our platform, AC2. A production harness is a whole engineered system around the LLM, with its own context management, tools, sandboxing, and control flows. Porting that into a new training runtime can be expensive and could introduce train-test mismatch, where the policy is optimized against a simulated harness and then struggles in production. All you need to do is swap out the harness’ LLM response endpoint to one provided by AC2, and expose a lightweight protocol for AC2 to initiate and grade rollouts; the trainer handles the rest.

You can now post-train a model inside your existing production harness with our platform, AC2. A production harness is a whole engineered system around the LLM, with its own context management, tools, sandboxing, and control flows. Porting that into a new training runtime can be expensive and could introduce train-test mismatch, where the policy is optimized against a simulated harness and then struggles in production. All you need to do is swap out the harness’ LLM response endpoint to one provided by AC2, and expose a lightweight protocol for AC2 to initiate and grade rollouts; the trainer handles the rest.

Applied Compute

180,495 views • 17 days ago

💬 We get asked What should I do if I don’t have my own trading ideas yet? ❕ Answer from a GT App Specialist: You don’t need to be a professional strategist to start trading. GT App’s AI layer generates new strategy ideas every day that you can immediately explore and test. 🔸 Daily AI-generated strategies Advanced LLMs build fresh trading strategies daily. The LLM Builder creates complete strategy setups that you can instantly optimize to see how they would have performed. 🔸 Pick and test in seconds Inside the app you’ll find AI strategy cards labeled by the LLM that generated them. Select a strategy, run an optimization, and instantly review metrics like win rate, trade history, and profit performance. 🔸 Or build a strategy directly in Telegram You can also generate and test strategies through our Telegram bot. Just open @gt_ai_trading_bot, request a strategy for a trading pair, and the AI will build and backtest it for you. Explore AI-generated strategies 👉

💬 We get asked What should I do if I don’t have my own trading ideas yet? ❕ Answer from a GT App Specialist: You don’t need to be a professional strategist to start trading. GT App’s AI layer generates new strategy ideas every day that you can immediately explore and test. 🔸 Daily AI-generated strategies Advanced LLMs build fresh trading strategies daily. The LLM Builder creates complete strategy setups that you can instantly optimize to see how they would have performed. 🔸 Pick and test in seconds Inside the app you’ll find AI strategy cards labeled by the LLM that generated them. Select a strategy, run an optimization, and instantly review metrics like win rate, trade history, and profit performance. 🔸 Or build a strategy directly in Telegram You can also generate and test strategies through our Telegram bot. Just open @gt_ai_trading_bot, request a strategy for a trading pair, and the AI will build and backtest it for you. Explore AI-generated strategies 👉

GT Protocol

32,277 views • 5 months ago

Google dropped another banger! They just released a comprehensive white-paper on AgentOps - the missing piece between building AI agents and actually shipping them to production. Here's the reality: Building an AI agent takes minutes. Making it production-ready? That's where 80% of the real work begins. Google's "Prototype to Production" guide tackles this exact problem. The framework has three core pillars: 1. Evaluation-Gated Deployment: No agent reaches users without passing tests. Build a "golden dataset" that validates behavior, not just functionality. This catches what unit tests miss - agents choosing wrong tools or hallucinating responses. 2. Automated CI/CD for Agents: Test in stages: pre-merge checks for fast feedback, staging for load testing, then gated production. Version everything: prompts, tools, configs, evaluation datasets. 3. Observe → Act → Evolve Loop Production isn't the finish line. Monitor through logs, traces, and metrics. Act with circuit breakers and human escalation. Evolve by turning production failures into test cases. The best part? They released the Agent Starter Pack - a template with CI/CD, Terraform deployment, and built-in observability. Helps you spin up an evaluation pipeline in minutes. The guide also talks about the two major protocols and how they can work together. ↳ MCP for tool integration ↳ A2A for agent collaboration If you're shipping agents to production, you should read this. I've shared the full white-paper in the next tweet!

Google dropped another banger! They just released a comprehensive white-paper on AgentOps - the missing piece between building AI agents and actually shipping them to production. Here's the reality: Building an AI agent takes minutes. Making it production-ready? That's where 80% of the real work begins. Google's "Prototype to Production" guide tackles this exact problem. The framework has three core pillars: 1. Evaluation-Gated Deployment: No agent reaches users without passing tests. Build a "golden dataset" that validates behavior, not just functionality. This catches what unit tests miss - agents choosing wrong tools or hallucinating responses. 2. Automated CI/CD for Agents: Test in stages: pre-merge checks for fast feedback, staging for load testing, then gated production. Version everything: prompts, tools, configs, evaluation datasets. 3. Observe → Act → Evolve Loop Production isn't the finish line. Monitor through logs, traces, and metrics. Act with circuit breakers and human escalation. Evolve by turning production failures into test cases. The best part? They released the Agent Starter Pack - a template with CI/CD, Terraform deployment, and built-in observability. Helps you spin up an evaluation pipeline in minutes. The guide also talks about the two major protocols and how they can work together. ↳ MCP for tool integration ↳ A2A for agent collaboration If you're shipping agents to production, you should read this. I've shared the full white-paper in the next tweet!

Akshay 🚀

37,187 views • 9 months ago

THIS DEVELOPER USED OPENCLAW AGENTS TO RUN HIS B2B BUSINESS VIA TELEGRAM AND MADE $15,000/MONTH he doesn't write prompts from scratch or use generic browser interfaces. he runs a multi-agent framework through a mobile chat. the agents write code, test deployments, and update sites in real-time while he just hits approve the setup is straightforward: - spin up Coolify on a free cloud instance to host your own self-hosted agent panels - link the agent loop to a Telegram gateway to approve code edits from your phone - deploy specialized skill files directly to limit token waste and context decay - containerize the terminal execution using Docker to prevent security breaches if you are still running local agents without container safety, you are leaving money on the table. read the 30-day battle between OpenClaw and Hermes Agent to see who actually wins in production Full breakdown and migration playbook ↓

THIS DEVELOPER USED OPENCLAW AGENTS TO RUN HIS B2B BUSINESS VIA TELEGRAM AND MADE $15,000/MONTH he doesn't write prompts from scratch or use generic browser interfaces. he runs a multi-agent framework through a mobile chat. the agents write code, test deployments, and update sites in real-time while he just hits approve the setup is straightforward: - spin up Coolify on a free cloud instance to host your own self-hosted agent panels - link the agent loop to a Telegram gateway to approve code edits from your phone - deploy specialized skill files directly to limit token waste and context decay - containerize the terminal execution using Docker to prevent security breaches if you are still running local agents without container safety, you are leaving money on the table. read the 30-day battle between OpenClaw and Hermes Agent to see who actually wins in production Full breakdown and migration playbook ↓

marfin

26,654 views • 2 months ago

20 days ago, I connected Claude Code to my newly created instagram handle.. I gained 4.3M views and 6500+ followers in less than a month [ i post Ai generated animated stories ] Full workflow: i let claude study my account before i write another reel.. This is the cleanest content workflow i've built on claude. give it your IG first. 4 prompts handle the rest.. niche research, the reel script, the hook, and the daily automation.. the whole loop is basically, give claude your IG → find what's working → write retention-optimized scripts → engineer the hook → automate the daily output.. ▫️ Setup: give claude your instagram open claude code. claude code has a built-in web tool that browses any public URL. or install any agentic browser like Browser Harness or Firecrawl or Comet browser paste this with your handle filled in: "Browse and pull the last 30 reels and posts. Analyze my recurring topics, top-performing hooks, formats, and engagement patterns. Then map out my actual audience and what they consistently respond to." claude reads your profile, pulls every reel down, and now has the context to personalize every prompt below to YOUR account, not a generic niche. if you're on claude desktop, the same works with firecrawl MCP connected. ▫️ Prompt 1 find what actually goes viral in your niche: "Analyze the highest-performing Instagram Reels, TikToks, and Reddit posts in the [niche] niche from the last 30 days. Identify repeating hooks, visual styles, emotional triggers, and content formats that consistently generate high engagement. Then summarize the 5 strongest content angles optimized for AI-generated content and short-form videos." run this after the setup. you get 5 angles backed by what's already working in your niche, cross-checked against what's already working on YOUR account. ▫️ Prompt 2 write a high-retention reel script "Write a short-form Instagram Reel script about [topic] with an aggressive hook in the first 2 seconds. Create immediate curiosity, tension, or controversy to stop scrolling, then deliver a fast and satisfying payoff. Keep it under 30 seconds and optimize the structure for watch time, replays, comments, and shares. Finish with a subtle CTA." the line that matters: "optimize the structure for watch time, replays, comments, and shares." claude writes for the metrics, not just the word count. ▫️ Prompt 3 engineer better hooks "Study the top-performing Reels in [niche] and break down the hook structure, pacing, and emotional triggers used in the first 3 seconds. Then generate 5 new hook variations that are even more curiosity-driven, emotionally charged, and optimized to stop scrolling instantly. Focus on triggers like surprise, fear, ego, urgency, or desire." most reels die in the first 2 seconds. this prompt has claude reverse-engineer what already works, then give you 5 sharper versions to swap in. ▫️ Prompt 4 automate the whole workflow "Build a complete AI-powered content workflow for Instagram in the [niche] niche. The system should identify trending topics daily, generate high-retention scripts, create matching AI visuals, turn them into short-form videos, and generate optimized captions and hashtags. Structure everything as a repeatable workflow designed for consistent daily posting and growth." once the niche and script structure are validated, this turns it into a daily loop. one prompt that handles topic → script → visual → video → caption. these 4 prompts are the building blocks. the setup is what makes them yours. your real value is in the [niche] you plug in. content workflow built in one weekend, daily posting on autopilot from monday.

Axel Bitblaze 🪓

201,149 views • 2 months ago

Claude + Arcads + Whop You have no idea how much you can make in 2026. Literally sell anything on autopilot. With AI ugc studio in arcads, you can launch an army of AI influencers selling your products 24/7 across socials. I built an n8n workflow that: 1. Generates AI characters at scale 2. Write scripts using Claude 3. Produces ugc videos for your product 4. Schedules it across social channels Run a/b tests. Use affiliate links. Diversify with dedicated TikTok accounts. Fast. No casting, contracts, or 30-day cycles. I have been testing prompts that make culturally relevant cum attractive influencers and running them on ghost accounts. Will share the prompts and results in the next thread. This single n8n workflow can make you $30k/mo. To get it for FREE, Like + comment "arcads" and I'll DM you.

Claude + Arcads + Whop You have no idea how much you can make in 2026. Literally sell anything on autopilot. With AI ugc studio in arcads, you can launch an army of AI influencers selling your products 24/7 across socials. I built an n8n workflow that: 1. Generates AI characters at scale 2. Write scripts using Claude 3. Produces ugc videos for your product 4. Schedules it across social channels Run a/b tests. Use affiliate links. Diversify with dedicated TikTok accounts. Fast. No casting, contracts, or 30-day cycles. I have been testing prompts that make culturally relevant cum attractive influencers and running them on ghost accounts. Will share the prompts and results in the next thread. This single n8n workflow can make you $30k/mo. To get it for FREE, Like + comment "arcads" and I'll DM you.

Kritarth Mittal | Soshals

144,832 views • 7 months ago

Which local models can actually handle tool calling? I built a framework to find out. 15 scenarios. 12 tools. Mocked responses. Temperature 0. No cherry-picking. Tested every Qwen3.5 size from 0.8B to 397B, and since some of you asked after the distillation tests: yes, I included Jackrong's Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled too. Only two models went all green: the 27B dense and the distilled 27B. The 397B? Failed two tests. The 122B? Failed one. The 35B? Failed two. The timed-out results — mostly on the smaller models, are cases where the model got stuck in a loop, repeating the same tool call until it hit the 30-second limit. The test that exposed the most models: "Search for Iceland's population, then calculate 2% of it." Simple, but 35B, 122B, and 397B all used a rounded number from memory instead of the actual search result. They didn't trust their own tool output. Small models hallucinate data. Big models ignore data. The 27B just threaded it through.

Which local models can actually handle tool calling? I built a framework to find out. 15 scenarios. 12 tools. Mocked responses. Temperature 0. No cherry-picking. Tested every Qwen3.5 size from 0.8B to 397B, and since some of you asked after the distillation tests: yes, I included Jackrong's Qwen3.5-27B-Claude-4.6-Opus-Reasoning-Distilled too. Only two models went all green: the 27B dense and the distilled 27B. The 397B? Failed two tests. The 122B? Failed one. The 35B? Failed two. The timed-out results — mostly on the smaller models, are cases where the model got stuck in a loop, repeating the same tool call until it hit the 30-second limit. The test that exposed the most models: "Search for Iceland's population, then calculate 2% of it." Simple, but 35B, 122B, and 397B all used a rounded number from memory instead of the actual search result. They didn't trust their own tool output. Small models hallucinate data. Big models ignore data. The 27B just threaded it through.

stevibe

432,969 views • 5 months ago

whoever leaked this has bigger balls than sense SpaceXAI shipped five hireable workers for $200 a month, then wrote the catch into its own Grok Bot documentation and left the page up: all five run on one computer, so one sign-in hands the browser session, the files and the command-line credentials to every one of them the NSA, CISA and the cyber agencies of the UK, Canada, Australia and New Zealand had published the opposite instruction 103 days earlier: no broad or unrestricted access, low-risk and non-sensitive work only i ran four of mine on one account for a week, counting what each could reach: eleven signed-in apps, one browser profile, and deleting a bot left all of it standing Grok Bot is worth hiring five times over, and you can draw its blast radius before the second one exists: - sign in for the bot that needs the site, then open the others and see what they reach: that session is theirs the moment it exists - give each bot its own account on the app, since the docs tell you in writing to stop using separate bots as a security boundary - put the stop line in the description, as an approval controls the proposed action and leaves whatever already ran where it landed - cap the spend outside the product, because there is no bot-specific spend cap yet and the audit view of what they did is still coming - keep the money and the customer replies in your own hands, and let the other four start from scratch each morning on work that cannot bite one sign-in is also why this pays: five names finish inside your real tools instead of handing you drafts to paste my take, and it is the uncomfortable one: your real limit on Grok Bot is how many logins you will put on one machine, and the hiring was always the easy half bookmark this, the five descriptions that let bots hand work to each other and the one folder that survives an update are written out in the article ↓

Argona

687,690 views • 8 days ago

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

The Visual Studio Code insiders version that just shipped and will ship in the next few days will come with an insane amount of new capabilities. A few highlights: - You can now run sub-agents in parallel. Yes, really. I even attached a video. - Major UX improvements for sub agents, especially visible in the chat window - A new search tool wrapped as a sub-agent that iteratively runs multiple search tools: semantic_search, file_search, grep_search Which connects nicely to the point above: multiple searches running in parallel, efficiently and fast - Anthropic’s Message API is now enabled by default - You can choose the model for the cloud agent (three available, all premium) - Extended thinking support when using the Claude cloud agent This is part of the broader multi-vendor cloud support under AgentsHQ I wrote about a few weeks ago - Tasks sent to the background agent (basically the CLI tool) now always run in isolation, each with its own git worktree - In a multi-repo workspace, assigning a task to a cloud agent prompts you to choose the target repo Same behavior when opening an empty workspace with no repo - Support for building an external index for files not supported by GitHub’s default indexing - UI/UX improvements for starting new sessions and switching between local / background / cloud agents - Skills are now first-class citizens, just like prompt files, with better UX indicating when a skill is loaded - Improved API for dynamic contribution of prompt files New V2 includes skills as part of the model. Curious to see the extensions that will leverage this - Finally, initial support for showing context usage percentage per session - Skills are enabled by default - Resizable chat window and session view. Small thing, but it was driving me crazy 😁 - A new integrated browser meant to replace the old simple browser Maybe the beginning of real browser use? - Better UI/UX for token streaming in chat - Ability to index external files not supported by GitHub There’s a lot more. Some of it hasn’t fully landed yet, but everything that has is already in Insiders. The next stable release should drop in early February. As usual, I’m just shocked by the volume of features this team ships every month. After the holiday slowdown, this one is shaping up to be a wild release.

Oren Melamed

29,555 views • 7 months ago

Claude Fable 5 is insane for voice-of-customer research 🤯 I just built a Claude Code skill that catches your customers quoting your own ads back to you. It reads your reviews, cross-references every recurring phrase against your website + ad copy, and sorts your "voice of customer" into three piles: Planted, category-standard, organic gold. All inside Claude Code. Perfect for DTC brands and creative strategists who brief ads off review mining. If you're pulling ad copy from your reviews, some of that language is real customer voice, some of it is your own tagline, and every time you re-use it, you're marketing to yourself a little harder. This skill breaks the loop: → Drop in any review export (Judge .me, Okendo, Amazon, Shopify) → It scrapes your site + ad copy automatically → Every recurring phrase gets 3 forensic tests (overlap, independence, category) → Verdicts come with receipts: counts, sources, confidence levels → Dark-mode dashboard + 5 ready-to-test hooks from the gold pile No API keys. No pip installs. No copy-paste prompt rituals. What you get: → The "planted" list — phrases you taught your customers (stop briefing off these) → The organic gold list — language customers use that your ads never have → 5 hooks built from real customer phrasing → A dashboard your whole team can read Runs 100% in Claude Code Want full playbook for free? > Like this post > Comment "Claude" And I'll send it over (must be following so I can DM)

Claude Fable 5 is insane for voice-of-customer research 🤯 I just built a Claude Code skill that catches your customers quoting your own ads back to you. It reads your reviews, cross-references every recurring phrase against your website + ad copy, and sorts your "voice of customer" into three piles: Planted, category-standard, organic gold. All inside Claude Code. Perfect for DTC brands and creative strategists who brief ads off review mining. If you're pulling ad copy from your reviews, some of that language is real customer voice, some of it is your own tagline, and every time you re-use it, you're marketing to yourself a little harder. This skill breaks the loop: → Drop in any review export (Judge .me, Okendo, Amazon, Shopify) → It scrapes your site + ad copy automatically → Every recurring phrase gets 3 forensic tests (overlap, independence, category) → Verdicts come with receipts: counts, sources, confidence levels → Dark-mode dashboard + 5 ready-to-test hooks from the gold pile No API keys. No pip installs. No copy-paste prompt rituals. What you get: → The "planted" list — phrases you taught your customers (stop briefing off these) → The organic gold list — language customers use that your ads never have → 5 hooks built from real customer phrasing → A dashboard your whole team can read Runs 100% in Claude Code Want full playbook for free? > Like this post > Comment "Claude" And I'll send it over (must be following so I can DM)

Mike Futia

10,266 views • 1 month ago

SERAPH: A new round of major events in Diablo is about to begin! 🚀 Each new event is a new opportunity. Let's first review the achievements SERAPH has made before. 🏆 Seraph Achievements: Genesis NFT (Weapons) 0.06e ==》0.628e 💎 SERAPH Priority Pass Freemint ==》3.2e 🚀 In traditional GameFi, the issue of the death spiral of the initial NFT launch is often encountered. However, SERAPH achieved a tenfold floor price increase with a public sale of 0.06e and has stably maintained this position. This highlights the commendable tokenomics of the project. 📈 Additionally, SERAPH's gameplay is quite impressive. The Twitter community shows a high level of engagement, with many players sharing their gaming experiences. During the third testing phase, players were not just playing SERAPH for profit but were immersed in the game, experiencing the storyline and the thrill of gameplay. 🎮🔥 New Major Event: After the War of Light, the life fortress of Seraficia faces energy depletion. As chaos spreads, the Archangel Seraf crafted the World Ring from the souls of all life, a powerful shield against chaotic attacks. 🌌🛡️ To protect the hard-won peace, fallen heroes are bestowed with new souls, transcending the limits of time and space. Barbarians, Valkyries, Witches, and Necromancers reassemble on the lands of Seraficia. Reborn SOULs, the legends of heroes. 🌟 In the shimmering rune light, we summon the most outstanding legion of heroes to face this gradually collapsing world. ⚔️ >>Hero Series NFT The newly launched Hero Series PFP NFT primarily features human heroes such as Barbarians, Valkyries, Witches, and Necromancers. It also includes angels, demons, and other otherworldly races, showcasing numerous characters developed in the game so far. 🎭 Specific Acquisition Methods: Hero Series NFTs will be sold through a limited whitelist and public sale lottery. Whitelist distribution channels include: 【Veteran Players Exclusive】- Ongoing support from our veteran players; 【DC Exclusive】- New players participating in this DC event; 【Community Collaboration】- Communities collaborating with Seraph. 【Veteran Players Exclusive】 Veteran players will have their whitelist distribution rules, which can be followed in later project updates. 【DC Exclusive】 New players can acquire whitelist status by participating in various activities in DC, accumulating Soul shards through DC events. Ways to earn them include: 1⃣️ Participate in team-organized activities and games. 2⃣️ Create unique and high-quality content, such as videos, posts, art, to earn Soul shards. 3⃣️ During flash community events/tasks, active community members may discover additional Soul shards. 4⃣️ Interact with the Engage bot in the 💠│engage channel. 5⃣️ Warriors making genuine contributions to the community may receive random Soul shard rewards. 【Community Collaboration】 Members of gaming communities can participate in special activities in their own communities to obtain whitelist spots. T1 labs will also have a series of events to secure whitelist spots. 🤝 SERAPH Elite Test Server Note: The test server does not involve digital assets, and all data will be uniformly deleted after the test. 🧪 To welcome the new content, the game's official release includes a small-scale test. This test is mainly for early experience and bug testing, providing an opportunity to explore new features. Players are encouraged to actively participate and explore to get a glimpse of what's to come. 🌐 Test Start Time: February 5, 2024, 11:00 (UTC+8) 📅 Test Method: Limited invitation using activation codes 💌 Invitation Targets: Top-ranking users from the previous three game tests; Active users involved in SERAPH community building. 🎉 Test Content: Significant structural and numerical optimization of the economic system; All-new concepts for NFT treasures; Comprehensive adjustments to skill balance and numerous detailed optimizations and bug fixes, etc. This test is open for PC/IOS/Android, allowing testing across multiple platforms simultaneously. 🕹️📱💻 Research Analyst: 比特狸狸

SERAPH: A new round of major events in Diablo is about to begin! 🚀 Each new event is a new opportunity. Let's first review the achievements SERAPH has made before. 🏆 Seraph Achievements: Genesis NFT (Weapons) 0.06e ==》0.628e 💎 SERAPH Priority Pass Freemint ==》3.2e 🚀 In traditional GameFi, the issue of the death spiral of the initial NFT launch is often encountered. However, SERAPH achieved a tenfold floor price increase with a public sale of 0.06e and has stably maintained this position. This highlights the commendable tokenomics of the project. 📈 Additionally, SERAPH's gameplay is quite impressive. The Twitter community shows a high level of engagement, with many players sharing their gaming experiences. During the third testing phase, players were not just playing SERAPH for profit but were immersed in the game, experiencing the storyline and the thrill of gameplay. 🎮🔥 New Major Event: After the War of Light, the life fortress of Seraficia faces energy depletion. As chaos spreads, the Archangel Seraf crafted the World Ring from the souls of all life, a powerful shield against chaotic attacks. 🌌🛡️ To protect the hard-won peace, fallen heroes are bestowed with new souls, transcending the limits of time and space. Barbarians, Valkyries, Witches, and Necromancers reassemble on the lands of Seraficia. Reborn SOULs, the legends of heroes. 🌟 In the shimmering rune light, we summon the most outstanding legion of heroes to face this gradually collapsing world. ⚔️ >>Hero Series NFT The newly launched Hero Series PFP NFT primarily features human heroes such as Barbarians, Valkyries, Witches, and Necromancers. It also includes angels, demons, and other otherworldly races, showcasing numerous characters developed in the game so far. 🎭 Specific Acquisition Methods: Hero Series NFTs will be sold through a limited whitelist and public sale lottery. Whitelist distribution channels include: 【Veteran Players Exclusive】- Ongoing support from our veteran players; 【DC Exclusive】- New players participating in this DC event; 【Community Collaboration】- Communities collaborating with Seraph. 【Veteran Players Exclusive】 Veteran players will have their whitelist distribution rules, which can be followed in later project updates. 【DC Exclusive】 New players can acquire whitelist status by participating in various activities in DC, accumulating Soul shards through DC events. Ways to earn them include: 1⃣️ Participate in team-organized activities and games. 2⃣️ Create unique and high-quality content, such as videos, posts, art, to earn Soul shards. 3⃣️ During flash community events/tasks, active community members may discover additional Soul shards. 4⃣️ Interact with the Engage bot in the 💠│engage channel. 5⃣️ Warriors making genuine contributions to the community may receive random Soul shard rewards. 【Community Collaboration】 Members of gaming communities can participate in special activities in their own communities to obtain whitelist spots. T1 labs will also have a series of events to secure whitelist spots. 🤝 SERAPH Elite Test Server Note: The test server does not involve digital assets, and all data will be uniformly deleted after the test. 🧪 To welcome the new content, the game's official release includes a small-scale test. This test is mainly for early experience and bug testing, providing an opportunity to explore new features. Players are encouraged to actively participate and explore to get a glimpse of what's to come. 🌐 Test Start Time: February 5, 2024, 11:00 (UTC+8) 📅 Test Method: Limited invitation using activation codes 💌 Invitation Targets: Top-ranking users from the previous three game tests; Active users involved in SERAPH community building. 🎉 Test Content: Significant structural and numerical optimization of the economic system; All-new concepts for NFT treasures; Comprehensive adjustments to skill balance and numerous detailed optimizations and bug fixes, etc. This test is open for PC/IOS/Android, allowing testing across multiple platforms simultaneously. 🕹️📱💻 Research Analyst: 比特狸狸

T1 Labs

12,546 views • 2 years ago

Fine-tune DeepSeek-OCR on your own language! (100% local) DeepSeek-OCR is a 3B-parameter vision model that achieves 97% precision while using 10× fewer vision tokens than text-based LLMs. It handles tables, papers, and handwriting without killing your GPU or budget. Why it matters: Most vision models treat documents as massive sequences of tokens, making long-context processing expensive and slow. DeepSeek-OCR uses context optical compression to convert 2D layouts into vision tokens, enabling efficient processing of complex documents. The best part? You can easily fine-tune it for your specific use case on a single GPU. I used Unsloth to run this experiment on Persian text and saw an 88.26% improvement in character error rate. ↳ Base model: 149% character error rate (CER) ↳ Fine-tuned model: 60% CER (57% more accurate) ↳ Training time: 60 steps on a single GPU Persian was just the test case. You can swap in your own dataset for any language, document type, or specific domain you're working with. I've shared the complete guide in the next tweet - all the code, notebooks, and environment setup ready to run with a single click. Everything is 100% open-source!

Fine-tune DeepSeek-OCR on your own language! (100% local) DeepSeek-OCR is a 3B-parameter vision model that achieves 97% precision while using 10× fewer vision tokens than text-based LLMs. It handles tables, papers, and handwriting without killing your GPU or budget. Why it matters: Most vision models treat documents as massive sequences of tokens, making long-context processing expensive and slow. DeepSeek-OCR uses context optical compression to convert 2D layouts into vision tokens, enabling efficient processing of complex documents. The best part? You can easily fine-tune it for your specific use case on a single GPU. I used Unsloth to run this experiment on Persian text and saw an 88.26% improvement in character error rate. ↳ Base model: 149% character error rate (CER) ↳ Fine-tuned model: 60% CER (57% more accurate) ↳ Training time: 60 steps on a single GPU Persian was just the test case. You can swap in your own dataset for any language, document type, or specific domain you're working with. I've shared the complete guide in the next tweet - all the code, notebooks, and environment setup ready to run with a single click. Everything is 100% open-source!

Akshay 🚀

126,122 views • 9 months ago

Here's what The Browser Company's AI eng & ML teams are working on for Dia right now: (This is a pitch to come work for us; info at end) 🤖 COMPUTER USE – we've built our own bespoke APIs on top of Chromium to optimize latency, accuracy, and cost of computer-using agents. Demo attached. Big breakthroughs here in recent weeks. 🛡️ ON-DEVICE MODELS – we've built our own custom infra to run everything from encoder-only models to full LLMs on device. It's cross-platform, supports LoRa adapters, and optimized for the GPU. This system preserves privacy and enables fast inference times. 🧠 MEMORY – with your permission, Dia automatically tailors your AI experiences to you, personally, based on the tabs you open while browsing normally every day. We're also bringing vertical memory to specific features. ♻️ DATA FLYWHEELS – our Fall/Winter P0 is to double-down on training custom models based on implicit signals from daily use of Dia. Dia should get smarter and more useful the more people use it. Whether via RL, auto-generated prompts, or otherwise. If this work sounds interesting to you please visit our jobs page or email careers@thebrowser.company. Hiring nearly every related role -- from ML engineers to people prototyping with AI and context/prompt writers -- everyone encouraged to apply!!

Here's what The Browser Company's AI eng & ML teams are working on for Dia right now: (This is a pitch to come work for us; info at end) 🤖 COMPUTER USE – we've built our own bespoke APIs on top of Chromium to optimize latency, accuracy, and cost of computer-using agents. Demo attached. Big breakthroughs here in recent weeks. 🛡️ ON-DEVICE MODELS – we've built our own custom infra to run everything from encoder-only models to full LLMs on device. It's cross-platform, supports LoRa adapters, and optimized for the GPU. This system preserves privacy and enables fast inference times. 🧠 MEMORY – with your permission, Dia automatically tailors your AI experiences to you, personally, based on the tabs you open while browsing normally every day. We're also bringing vertical memory to specific features. ♻️ DATA FLYWHEELS – our Fall/Winter P0 is to double-down on training custom models based on implicit signals from daily use of Dia. Dia should get smarter and more useful the more people use it. Whether via RL, auto-generated prompts, or otherwise. If this work sounds interesting to you please visit our jobs page or email [email protected]. Hiring nearly every related role -- from ML engineers to people prototyping with AI and context/prompt writers -- everyone encouraged to apply!!

Josh Miller

68,124 views • 1 year ago

I think that fast strides are the most powerful training stimulus for runners. For a glimpse at the physiology, I want to talk about my all-time favorite study. If we were playing FMK, I’d F this study, M this study, and K any study that is not this study. Published in 2018 in Physiological Reports, it had 20 trained athletes (14 men and 6 women) complete a 40-day intervention with 10 sessions involving 5-10 x 30 seconds with 3.5 minutes recovery, in conjunction with a 36% reduction in weekly volume. Before and after the intervention, participants did a time-to-exhaustion test, two 10km tests (one with glycogen depletion) preceded by 2 x 6 minute intervals at 60% of VO2 max, and a series of muscle biopsy and blood tests. Running economy during the 10k improved by 2.1% and during the intervals at 60% of VO2 max by 1.7%. In other words, the athletes improved at both hard efforts and easy ones. Meanwhile, there was no running economy change in the glycogen-depletion 10k, leading the authors to theorize that improvements were primarily related to changes in slow-twitch muscle fibers. Expression of protein dystrophin increased, which may enhance the ST-fiber structural integrity via the actin-myosin cross-bridge, improving running economy. In addition, the expression of UCP3 decreased, which may improve mitochondrial efficiency. In fast-twitch fibers, expression of the muscle protein SERCA1 decreased by 22%, which could reduce the energy turnover during exercise. Enzymatic activity and testosterone:cortisol ratio also went up. Don’t be discouraged, I only kinda know what a few of those words mean. The point is that as researchers introduce stride-like speed sessions and measure more variables, they often see major changes that may explain why Olympians are always doing strides. So when we use the term “running economy,” we’re actually incorporating dozens of these physiological variables, some of which we can measure and some of which we can’t, from the cells to the muscles to the circulatory and cardiovascular systems. Put it together, and getting faster is a wonderfully complex algorithm. Strides are one way to help solve it 💪

I think that fast strides are the most powerful training stimulus for runners. For a glimpse at the physiology, I want to talk about my all-time favorite study. If we were playing FMK, I’d F this study, M this study, and K any study that is not this study. Published in 2018 in Physiological Reports, it had 20 trained athletes (14 men and 6 women) complete a 40-day intervention with 10 sessions involving 5-10 x 30 seconds with 3.5 minutes recovery, in conjunction with a 36% reduction in weekly volume. Before and after the intervention, participants did a time-to-exhaustion test, two 10km tests (one with glycogen depletion) preceded by 2 x 6 minute intervals at 60% of VO2 max, and a series of muscle biopsy and blood tests. Running economy during the 10k improved by 2.1% and during the intervals at 60% of VO2 max by 1.7%. In other words, the athletes improved at both hard efforts and easy ones. Meanwhile, there was no running economy change in the glycogen-depletion 10k, leading the authors to theorize that improvements were primarily related to changes in slow-twitch muscle fibers. Expression of protein dystrophin increased, which may enhance the ST-fiber structural integrity via the actin-myosin cross-bridge, improving running economy. In addition, the expression of UCP3 decreased, which may improve mitochondrial efficiency. In fast-twitch fibers, expression of the muscle protein SERCA1 decreased by 22%, which could reduce the energy turnover during exercise. Enzymatic activity and testosterone:cortisol ratio also went up. Don’t be discouraged, I only kinda know what a few of those words mean. The point is that as researchers introduce stride-like speed sessions and measure more variables, they often see major changes that may explain why Olympians are always doing strides. So when we use the term “running economy,” we’re actually incorporating dozens of these physiological variables, some of which we can measure and some of which we can’t, from the cells to the muscles to the circulatory and cardiovascular systems. Put it together, and getting faster is a wonderfully complex algorithm. Strides are one way to help solve it 💪

David Roche

73,596 views • 1 year ago

nobody in that room realised what he just said microsoft's ceo told a conference that the frontier model you rent for $200 a month is the commodity, and someone has now measured what that costs everyone who assumed otherwise teams carrying 90 to 100% evaluation coverage reach excellent reliability 70.3% of the time, against 32.4% for teams sitting under half, on the same rented models i read the survey behind it twice because the sample is 500 enterprise teams rather than a vendor anecdote, and the spread holds across all of them this is Eval Engineering, and it is the part of the stack that stops being rented: - stop classifying behaviours as low-risk before you have data on them, because the 19.3% of teams who do take 2.3 times the production incidents and the intuition fails hardest exactly where behaviour is emergent - make the incident the source of the test: only 51.7% of teams turn an outage into a permanent regression, so half of all incident response gets paid for and then thrown away - budget coverage like continuous integration rather than like paperwork, since the pattern separating the elite teams is 70% coverage held together with 40% of development time spent on testing - gate the deploy on the eval instead of reporting the eval, because a threshold that cannot block a release is a dashboard with extra steps - keep the examiner private, since it encodes your own definition of correct: the model gets replaced from scratch twice a year and the examiner is the only asset that survives the swap - expect the incident rather than hoping to prevent it, because 84.9% of organisations hit one inside six months and only 8.4% report none, so detection speed is the real variable - review the gaps on a schedule and make the team justify an uncovered behaviour rather than defend a test that already exists the catch is what coverage actually costs, and it is not the tooling bill: the elite pattern spends 40% of development time on testing, which is the share of every sprint that stops being feature work that is the trade nobody puts in the quickstart, and it is why most teams stay at the coverage level where reliability lands at 32.4% bookmark this, the whole build sits in the article ↓

nobody in that room realised what he just said microsoft's ceo told a conference that the frontier model you rent for $200 a month is the commodity, and someone has now measured what that costs everyone who assumed otherwise teams carrying 90 to 100% evaluation coverage reach excellent reliability 70.3% of the time, against 32.4% for teams sitting under half, on the same rented models i read the survey behind it twice because the sample is 500 enterprise teams rather than a vendor anecdote, and the spread holds across all of them this is Eval Engineering, and it is the part of the stack that stops being rented: - stop classifying behaviours as low-risk before you have data on them, because the 19.3% of teams who do take 2.3 times the production incidents and the intuition fails hardest exactly where behaviour is emergent - make the incident the source of the test: only 51.7% of teams turn an outage into a permanent regression, so half of all incident response gets paid for and then thrown away - budget coverage like continuous integration rather than like paperwork, since the pattern separating the elite teams is 70% coverage held together with 40% of development time spent on testing - gate the deploy on the eval instead of reporting the eval, because a threshold that cannot block a release is a dashboard with extra steps - keep the examiner private, since it encodes your own definition of correct: the model gets replaced from scratch twice a year and the examiner is the only asset that survives the swap - expect the incident rather than hoping to prevent it, because 84.9% of organisations hit one inside six months and only 8.4% report none, so detection speed is the real variable - review the gaps on a schedule and make the team justify an uncovered behaviour rather than defend a test that already exists the catch is what coverage actually costs, and it is not the tooling bill: the elite pattern spends 40% of development time on testing, which is the share of every sprint that stops being feature work that is the trade nobody puts in the quickstart, and it is why most teams stay at the coverage level where reliability lands at 32.4% bookmark this, the whole build sits in the article ↓

Argona

16,482 views • 22 days ago