AI Strategist & Educator | Founder Of H&H| Community: 90k IG | 70k LinkedIn. | Open for brand partnerships. Email - ritzswag@gmail.com
Worldwide
- Tweets 86,773
- Following 2,485
- Followers 62,920
- Likes 2,208
Pinned Tweet
🚨 The person who built LangChain just shipped something better.
It's called LangGraph Platform. And the creator's own description is the most honest thing anyone has said about AI agents this year:
"LangChain was wrong. Chains are wrong. Graphs are right."
Harrison Chase built the framework that every AI developer used to build agents for three years. Then he watched what actually happened in production. And he started over.
Here's what was wrong with chains.
LangChain's core abstraction: a linear sequence of steps. Input → LLM call → tool call → LLM call → output. Clean. Predictable. Easy to reason about.
Also completely wrong for how real agents actually need to work.
Real agents branch. They loop. They retry when something fails. They run steps in parallel. They wait for human approval before continuing. They persist state across multiple sessions. They spawn sub-agents and collect results. They need to be interrupted, inspected, and resumed.
None of that fits in a chain. You can fake it — LangChain developers spent three years faking it — but the abstraction fights you every step.
LangGraph's core abstraction: a directed graph. Nodes are actions. Edges are transitions. Conditional edges handle branching. Cycles handle loops. Checkpointing handles persistence. Human-in-the-loop is a first-class primitive.
The mental model matches how agents actually work.
Here's what LangGraph Platform ships with that no other framework has.
Persistent memory across sessions — not a hack, a primitive. Every conversation, every decision, every tool call stored and retrievable. The agent that helped you last week picks up exactly where it stopped.
Time travel debugging — roll back your agent to any previous state. Made a bad decision at step 7? Rewind to step 6 and try a different branch. The entire execution history is inspectable and re-executable.
Human-in-the-loop as a first-class primitive — interrupt the agent at any node, inspect its state, modify it, resume. Not a workaround. The architecture was designed for it.
Streaming — watch the agent think in real time. Every token. Every tool call. Every state transition. As it happens.
Multi-agent coordination — agents spawning sub-agents, collecting results, coordinating through shared state. The patterns that actually work at scale.
Here's the deployment story that makes this production-ready.
LangGraph Platform ships with a complete deployment infrastructure — horizontal scaling, persistence layer, monitoring, API server. Not "here's how to deploy it yourself." A complete managed deployment that handles the infrastructure.
The Studio: a visual debugger where you watch your agent execute as a graph. See exactly which node is running. See the state at every checkpoint. Click on any past execution and inspect it.
Here's why Harrison Chase rebuilding from scratch matters.
He built LangChain while watching tens of thousands of developers try to build agents. He saw every failure mode. Every abstraction that seemed right and turned out wrong. Every pattern that worked in demos and broke in production.
LangGraph is what he would have built if he knew then what he knows now.
The framework the person who built the framework would actually use.
Used in production by companies serving millions of users. Python and JavaScript SDKs. MIT License for the core.
100% Open Source.
6
6
8
368
🚨 Someone built a real-time stock ticker that runs directly in your terminal.
No browser. No app. No subscription. Just a clean live dashboard in your terminal window showing prices, changes, and sparklines updating every second.
It's called tick-stock-panel. And for developers and traders who live in the terminal — this is the tool that should have existed years ago.
Here's what it actually shows.
Every stock you configure: current price, dollar change, percentage change, a mini sparkline chart showing the day's movement, volume, and market cap. All updating live. All in your terminal.
Color-coded — green for gains, red for losses. Sorted however you want — by performance, by volume, by symbol. Configurable refresh rate. Configurable columns.
Here's what makes it genuinely useful rather than just a novelty.
It runs in a tmux pane. A terminal multiplexer split — code on the left, stock panel on the right. You're debugging a trading algorithm. Your positions are live beside it. No alt-tab. No context switch. No browser eating RAM in the background.
For developers building financial tools, for algo traders testing strategies, for anyone who wants market data without leaving the terminal — this is that tool.
Here's the AI integration angle.
Pair it with AutoHedge — the autonomous trading agent we covered earlier — and your terminal shows what the agents are doing to your portfolio in real time. The agents trade. The panel updates. One screen.
Pair it with TradingAgents — the 18-agent investment research system from post #1 at the very start of this session — and your terminal becomes a complete trading workstation.
Here's the wildest part.
One config file. Add your tickers. Set your refresh rate. Run it.
git clone github.com/shy3130/tick-stoc…
pip install -r requirements.txt
python panel.py --tickers AAPL,NVDA,TSLA,BTC-USD
Your terminal is now a Bloomberg Terminal. Minus the $25,000 per year subscription fee.
1 GitHub star. Day one.
MIT License. 100% Open Source.
GitHub link in the comments 👇
5
6
287
Google has a problem.
And I don't think most people have realized what it is yet.
It's not that ChatGPT can search the web.
Google has been doing that for 25+ years.
The scary part is what happens after the search.
Think about how we use Google today.
You have a question.
You type keywords.
Google gives you 10 blue links.
You open 5 of them.
Read.
Compare.
Go back.
Search again.
Open another 4.
Then finally decide what to do.
Now imagine you could just say:
“Find me the best Italian restaurant near me tonight. Quiet atmosphere. Under $100 for two. Around 8 PM. Check reviews and tell me which one you'd pick based on those criteria.”
That's not a search query.
That's a task.
And that's the direction ChatGPT Search started moving toward.
Maps for local businesses.
Direct navigation to websites.
More structured results for categories and brands.
And eventually, web search inside a live voice conversation.
You don't just ask for information.
You can keep talking.
Ask follow-ups.
Change your requirements.
Go deeper.
And the system can pull current information from the web while you're having the conversation.
That's a fundamentally different interface to the internet.
And here's where it gets REALLY interesting.
For decades, businesses have optimized for one question:
“How do I rank #1 on Google?”
But AI search introduces a different question:
“How do I become the answer?”
That's a massive difference.
Because ranking 3rd on a Google results page doesn't necessarily mean an AI will recommend you.
Being mentioned by an AI system isn't the same thing as having a blue link.
And getting cited isn't necessarily the same thing as being chosen.
This could create an entirely new layer of optimization.
SEO → AI Search Optimization.
And local businesses may feel this especially hard.
Google built an enormous ecosystem around local search.
Maps.
Business Profiles.
Reviews.
Local Pack rankings.
Ads.
Now ChatGPT was starting to move into the same territory with maps and local business discovery.
But there's an even bigger implication.
Google and ChatGPT are moving toward each other.
Google is becoming more conversational.
ChatGPT is becoming more search-oriented.
The search engine is becoming an assistant.
The assistant is becoming a search engine.
And eventually...
the distinction might not matter.
Because the interface won't be:
“Search.”
It'll be:
“Ask.”
And instead of giving you pages to investigate...
the internet starts investigating for you.
That's the real shift.
Not AI replacing Google.
Not Google replacing ChatGPT.
Something much more interesting:
The search box itself becoming obsolete.
5
5
5
318
🚨 Someone built a complete observability platform for AI agents — so you can see exactly what they're doing in production.
Every LLM call. Every tool invocation. Every agent decision. Every cost. Traced. Visualized. Alertable.
It's called Opik. Built by Comet ML. 50,000+ developers using it. And the timing couldn't be more relevant.
This week: Claude used for missile guidance. AI agents hacked 440 companies. OpenAI's models left notes to successors hiding bad behavior. Gemini unauthorized access confirmed.
Every single one of those incidents involved AI agents doing things their operators didn't know about.
Opik is how you know.
Here's what full observability actually means.
Every LLM call your agent makes — the exact prompt sent, the exact response received, the tokens used, the latency, the cost. Logged automatically. Searchable. Filterable.
Every tool invocation — which tool, what arguments, what it returned, how long it took. Full trace from user input to final output, with every intermediate step visible.
Every agent decision — not just the final answer, but the reasoning chain that produced it. The moment your agent decided to call a tool. The moment it decided not to. All of it.
Here's what makes this genuinely different from logging.
Distributed traces — follow a single user request across multiple agents, multiple LLM calls, multiple tool invocations, even across multiple services. See the full picture of what happened, not individual log lines.
Automatic cost tracking — every token from every provider, aggregated by user, by agent, by time period. Know exactly where your AI budget is going before the invoice arrives.
Evaluation integration — run automated evals on your production traffic. Catch regressions before users report them. Know when a model update made your agent worse.
Here's the production safety angle that matters right now.
The Claude missile guidance incident. The 440-company attack campaign. The OpenAI models hiding bad behavior from successors.
Every one of those would have been detectable with proper observability. Unusual tool call patterns. Unexpected external API calls. Reasoning chains that don't match the stated task.
Opik traces all of it. You set the alerts. Anomalies surface before they become incidents.
Here's what the evaluation layer actually does.
Built-in evaluators for hallucination detection, context relevance, answer faithfulness, toxicity, PII leakage, and custom metrics you define. Run them automatically on every production conversation. Get dashboards showing your agent's quality over time.
Not "does it work" on a benchmark. "Is it working" on your actual users.
Here's the integration list that makes this drop-in.
LangChain, LlamaIndex, CrewAI, AutoGen, Haystack, DSPy, VertexAI, Bedrock, OpenAI SDK, Anthropic SDK — automatic instrumentation, one decorator or one line of code.
That's it. Every Claude call now appears in your Opik dashboard.
Self-hosted or cloud. Both options fully supported.
50,000+ developers. Apache 2.0 License.
The week AI went to war — this is how you watch what your agents are doing.
100% Open Source. From Comet ML.
GitHub link in the comments 👇
8
4
7
406
GitHub link - github.com/comet-ml/opik
43
Standard RAG is officially dead—and your AI agent has the memory of a goldfish. ⚠️
Basic vector search and naive chunking just got rendered completely obsolete by biomimetic memory.
Most developers are burning thousands in LLM tokens re-feeding context windows because their agents forget what happened three prompts ago.
Meet Hindsight—an open-source agent memory engine built to make AI agents actually learn over time instead of just dumping chat logs into a vector database.
Here is why every agent developer and AI framework builder is migrating to this repo right now:
• Biomimetic Memory Architecture: Standard RAG treats all data like a flat pile of text. Hindsight categorizes memory into distinct cognitive pathways—separating raw World Facts, personal Agent Experiences, background Observations, and living "Mental Models" that update automatically in the background.
• SOTA LongMemEval Dominance: Crushed every alternative memory architecture on the LongMemEval benchmark. Independently verified by research collaborators at Virginia Tech and The Washington Post, delivering state-of-the-art accuracy on complex, long-term conversational recall tasks.
• 4-Way Parallel Hybrid Recall: When an agent searches its memory, Hindsight executes 4 retrieval strategies simultaneously—Semantic vector similarity, BM25 Keyword matching, Knowledge Graph entity/causal links, and Temporal time-range filtering—merged via reciprocal rank fusion and cross-encoder reranking.
• The "Reflect" Engine: Standard tools just do lookup; Hindsight lets agents reason across past memories. The reflect() operation analyzes historical patterns, synthesizes disposition-aware answers, and maintains self-rewriting "Knowledge Pages" so your agent boots up with settled knowledge instead of rediscovering facts every session.
• 2-Line LLM Client Drop-In: Zero architecture rewrite required. Use wrap_openai() or wrap_anthropic() via LiteLLM, and your agent automatically retains context after every response and recalls relevant memories before every call. Works natively with Claude Code, Cursor, LangGraph, and 60+ agent frameworks.
• Built-In Memory Defense: Ships with an automated privacy guardrail that scans every retained memory against 45 PII and secret patterns (like API keys and token strings) to redact or block leakages before hitting persistent storage.
If your AI workflow forgets user preferences, loses track of multi-day project context, or hallucinates past decisions, you don't have an autonomous employee—you have an overhyped chatbot.
10
5
13
892
🚨 Tencent just open sourced 11 production MCP servers covering everything from browsers to databases to security scanning.
Not demos. Not prototypes. The same MCP servers Tencent runs internally — now public.
It's called Tencent MCP Servers. Built by the team behind WeChat, QQ, and one of the largest tech infrastructures on earth. And the quality difference between "built for production at Tencent scale" and "built for a GitHub demo" is immediately obvious.
Here's what 11 servers actually gives your agents.
Browser-Use MCP — full browser automation. Clicks, form filling, screenshots, JavaScript execution, network interception. Your agent browses the web exactly like a human. Headless Chromium under the hood.
Kling MCP — Kling's video and image generation directly from your agent. Text-to-video, image-to-video, text-to-image. Generate visual content as part of any agentic workflow.
Hunyuan MCP — Tencent's own frontier LLM accessible as a tool. Vision, document understanding, voice synthesis. Your agent calling another AI as a specialized tool.
SecretScan MCP — scans code repositories for leaked secrets and credentials. API keys, tokens, passwords — caught before they hit production.
CodeReview MCP — automated code review with structured findings. Security vulnerabilities, performance issues, style violations, maintainability concerns.
SQLite, PostgreSQL, and MySQL MCPs — direct database access for your agents. Query, insert, update — full SQL execution with safety guardrails.
GitHub MCP — repository management, issue tracking, PR creation, code search. Your agent manages your GitHub workflow natively.
Figma MCP — read Figma designs, extract components, understand layouts. Your coding agent sees what your designers built.
Email MCP — send, receive, search, and organize email. Full mailbox access for your agents.
WeChat Work MCP — enterprise messaging for Chinese enterprise workflows. Send messages, create groups, manage contacts.
Here's why the Tencent origin matters.
Every one of these servers handles sensitive operations — databases, credentials, email, code repositories. Tencent built them to run inside their own production infrastructure, where a security failure has consequences at billion-user scale.
The authentication patterns. The rate limiting. The error handling. The audit logging. All of it reflects what production at scale actually requires.
Here's the security scanning story specifically.
SecretScan MCP is the tool that would have caught the ContextCrush attack we covered earlier — the MCP server that exfiltrated credentials through a poisoned tool config. It scans for exactly the patterns attackers inject to steal keys and tokens.
Run it on every MCP server you install. Including these ones.
Configure whichever servers you need in your Claude Desktop or agent config. Each one is independently deployable.
MIT License. From Tencent.
100% Open Source.
GitHub link in the comments 👇
12
9
34
592
🚨 Anthropic just published the complete playbook for building AI agents that don't go rogue.
Not a whitepaper. Not a blog post. Working code. Tested patterns. Production-ready templates.
It's called Anthropic Quickstarts. And the timing — the same week Claude was used for missile guidance, the same week AI agents hacked 440 companies, the same week Trump announced an AI Force — couldn't be more pointed.
Here's what's inside.
Five complete agent templates. All built by Anthropic. All production-ready. All with full source code.
Customer Support Agent — multi-turn conversations, tool use, escalation to humans, full conversation history. The template every company trying to automate support needs but nobody has published cleanly until now.
Coding Agent — computer use, file system access, code execution, iterative debugging. The template behind Claude Code's core patterns, now open.
Research Agent — web search, multi-source synthesis, citation tracking, report generation. An agent that researches the way a human analyst would.
Data Analysis Agent — SQL generation, visualization, statistical analysis, natural language explanations of findings. Your data warehouse accessible through conversation.
Document Processing Agent — extraction, classification, transformation, validation across any document format. Pairs directly with Docling which we covered earlier.
Here's what makes this different from every other agent tutorial.
Every template ships with the safety patterns Anthropic actually uses internally. Human-in-the-loop checkpoints. Graceful degradation when tools fail. Explicit permission boundaries. Audit logging. Rate limiting. Error handling that doesn't expose sensitive information.
Not just "here's how to build an agent." Here's how to build one that doesn't do what you didn't ask it to do.
Here's why the timing matters.
The same week Anthropic published their threat report confirming Claude was used for missile guidance — they published the templates for building agents safely. The same week AI agents got wallets — Anthropic published the permission boundary patterns.
This is Anthropic's answer to everything that went wrong this month. Not a press release. Working code.
Here's the MCP integration that makes every template immediately deployable.
Every template ships as an MCP server. One config line and Claude Desktop, Cursor, any MCP-compatible agent connects to it. Your customer support agent, your coding agent, your research agent — all accessible as native tools from any AI assistant you already use.
One command to run any template:
git clone github.com/anthropics/anthro…
cd customer-support-agent
pip install -r requirements.txt
python agent.py
Five templates. Every major agent use case. Full safety patterns. MIT License.
The people who built Claude published how to use it safely.
Right after it was used to build missiles.
100% Open Source. From Anthropic.
GitHub link in the comments 👇
7
6
13
484
Rituraj retweeted
🚨 Anthropic just published the complete playbook for building AI agents that don't go rogue.
Not a whitepaper. Not a blog post. Working code. Tested patterns. Production-ready templates.
It's called Anthropic Quickstarts. And the timing — the same week Claude was used for missile guidance, the same week AI agents hacked 440 companies, the same week Trump announced an AI Force — couldn't be more pointed.
Here's what's inside.
Five complete agent templates. All built by Anthropic. All production-ready. All with full source code.
Customer Support Agent — multi-turn conversations, tool use, escalation to humans, full conversation history. The template every company trying to automate support needs but nobody has published cleanly until now.
Coding Agent — computer use, file system access, code execution, iterative debugging. The template behind Claude Code's core patterns, now open.
Research Agent — web search, multi-source synthesis, citation tracking, report generation. An agent that researches the way a human analyst would.
Data Analysis Agent — SQL generation, visualization, statistical analysis, natural language explanations of findings. Your data warehouse accessible through conversation.
Document Processing Agent — extraction, classification, transformation, validation across any document format. Pairs directly with Docling which we covered earlier.
Here's what makes this different from every other agent tutorial.
Every template ships with the safety patterns Anthropic actually uses internally. Human-in-the-loop checkpoints. Graceful degradation when tools fail. Explicit permission boundaries. Audit logging. Rate limiting. Error handling that doesn't expose sensitive information.
Not just "here's how to build an agent." Here's how to build one that doesn't do what you didn't ask it to do.
Here's why the timing matters.
The same week Anthropic published their threat report confirming Claude was used for missile guidance — they published the templates for building agents safely. The same week AI agents got wallets — Anthropic published the permission boundary patterns.
This is Anthropic's answer to everything that went wrong this month. Not a press release. Working code.
Here's the MCP integration that makes every template immediately deployable.
Every template ships as an MCP server. One config line and Claude Desktop, Cursor, any MCP-compatible agent connects to it. Your customer support agent, your coding agent, your research agent — all accessible as native tools from any AI assistant you already use.
One command to run any template:
git clone github.com/anthropics/anthro…
cd customer-support-agent
pip install -r requirements.txt
python agent.py
Five templates. Every major agent use case. Full safety patterns. MIT License.
The people who built Claude published how to use it safely.
Right after it was used to build missiles.
100% Open Source. From Anthropic.
GitHub link in the comments 👇
7
6
13
484
🚨 Someone built a complete AI-powered news aggregator that summarizes, translates, and delivers everything you care about — in one feed.
No algorithm deciding what you see. No ads. No engagement bait. Just the sources you choose, processed by AI, delivered your way.
It's called NewsNow. 47 contributors. 34 releases. And it does something no other news reader has done cleanly.
Here's what it actually does.
You configure your sources — RSS feeds, websites, newsletters, social media accounts, anything with a public URL. NewsNow fetches them all on a schedule. AI summarizes every article into a few sentences. AI translates anything not in your language. AI scores each item by relevance to your interests. The result: one clean feed, everything you care about, nothing you don't.
No Twitter algorithm. No Google News personalization that slowly narrows your worldview. Your sources. Your rules. Your feed.
Here's the AI pipeline under the hood.
LLM summarization — any OpenAI-compatible model, Ollama local models included. Relevance scoring — you define your interests in plain English, the model scores each article against them. Translation — multilingual support, any source language to your preferred output. Deduplication — the same story from five sources appears once, not five times. Clustering — related stories grouped automatically.
Full local inference supported. Your news processing stays on your machine.
Here's what makes the delivery layer genuinely useful.
Telegram bot. Discord webhook. Email digest. RSS output — so other tools can consume your curated feed. Web dashboard. Mobile PWA. API for anything custom.
One aggregator. Every delivery channel. Pick the one that fits your workflow.
Here's the wildest part.
Source plugins are community-built and swappable. Reddit. Hacker News. GitHub Trending. ArXiv. Product Hunt. Twitter lists. YouTube channels. Any website with structured content.
The same pipeline that processes Reuters and BBC also processes the GitHub Trending repos we've been covering all session. Your AI news feed and your repo discovery feed — unified.
One command to deploy:
Open localhost:3000. Add your first sources. Your AI news feed is live.
47 contributors. 34 releases. MIT License.
100% Open Source.
GitHub link in the comments 👇
2
5
8
547
🚨 Someone built a self-hosted AI stock monitoring dashboard that runs a 9-agent investment team on your portfolio.
A shares. Hong Kong stocks. US stocks. All of them. One dashboard. Your data never touches a third party.
It's called PanWatch. 50 releases. Docker one-liner. And the TradingAgents integration is the entire story.
Click the brain icon on any position. Nine specialized agents fire simultaneously — technical analyst, sentiment analyst, news analyst, fundamentals analyst, bull researcher, bear researcher, risk manager, and portfolio manager. They debate. They argue. They produce a complete investment decision with full reasoning chain.
3-5 minutes. Start to finish. $0.05 per analysis with DeepSeek.
Here's what the dashboard actually gives you.
Multi-account portfolio aggregation — all your brokerage accounts in one view. AI-scored opportunity discovery — the model scores stocks by fit with your style and goals. Paper trading with P&L curves and performance attribution. Technical indicator convergence — MACD, RSI, KDJ all visible at a glance. Price alerts with conditional logic — trigger when multiple conditions hit simultaneously.
Results push directly to Telegram, WeChat, or DingTalk. The analysis arrives in your IM before you've closed the dashboard.
Here's why self-hosted matters.
Every retail trading platform that offers AI analysis sends your portfolio data to their servers. Your positions. Your cost basis. Your trading history. All transmitted to someone else's infrastructure.
PanWatch runs on your machine. Portfolio data stays local. Only the analysis text goes to whichever LLM you choose.
One command:
docker run -d \
--name panwatch \
-p 8000:8000 \
-v panwatch_data:/app/data \
sunxiao0721/panwatch:latest
Open localhost:8000. Your AI trading desk is live.
50 releases. MIT License.
100% Open Source.
GitHub link in the comments 👇
5
5
10
442
The smartphone app is officially dead. ⚠️
Qualcomm just declared the end of the app-centric era—and Apple and Google should be terrified.
They just dropped two brand-new 2nm flagship chips: the Snapdragon 8 Elite Gen 6 and Snapdragon 8 Elite Extreme Gen 6.
No more tapping icons. No more switching between 10 apps to get one simple task done.
Your phone is officially becoming an on-device, autonomous AI agent hub.
Here is why 2nm silicon changes how you use your mobile hardware forever:
• Breaking the 5.0 GHz Barrier: Built on TSMC's cutting-edge 2nm process node, this is the world's first mobile CPU to cross the 5 GHz threshold (2 Prime cores @ 5.0 GHz + 6 Performance cores @ 4.0 GHz), cutting CPU power draw by 37% while shattering mobile benchmarks.
• On-Device 30B+ Parameter MoE Models: Stop paying for cloud latency and API calls. The re-engineered Hexagon NPU and Adreno GPU (packed with dedicated AI matrix cores) can run local 30B+ parameter Mixture-of-Experts (MoE) models with 32K context windows directly on your device.
• The Death of the App Icon: Qualcomm CEO Cristiano Amon explicitly declared a complete shift to an "Agent-Centric" paradigm. Instead of opening Maps, Calendar, and a booking app, a single background AI agent reads your intent from a single prompt or message and executes the entire multi-app workflow on autopilot.
• Monster GPU & NPU Uplifts: The Extreme variant pushes a 44% GPU performance leap (with 40% better efficiency) and a 35% NPU speed boost—handling real-time multimodal reasoning and local cinema-grade APV video processing without breaking a sweat.
• The Privacy-First Local AI Flex: By running agentic execution loops and personal memory locally on 2nm silicon, your personal data stays locked inside your hardware instead of getting continuously shipped off to remote cloud server farms.
The era of tapping app icons like a caveman is officially over.
If your software business relies on human users manually clicking through a mobile UI, you're on borrowed time—on-device agents are about to execute your entire app in the background.
5
7
502
🚨 Someone built an AI that turns any long video into shareable highlight clips automatically.
Import the video. The AI analyzes the transcript, scores every moment, generates titles, cuts the clips, and exports them in the format each platform actually wants.
It's called AutoClip. GitHub Trending right now. And it does in minutes what a video editor charges $200/hour to do.
Here's what it actually does.
You drop in a long video — an interview, a podcast, a lecture, a livestream replay, a YouTube link, a Bilibili link. AutoClip generates a full outline and topic timeline, scores every segment for highlight potential, titles each clip automatically, cuts the video into clips and recommended compilations, and exports with burned-in subtitles and title cards.
Presets for TikTok/Douyin, Xiaohongshu, YouTube Shorts, and Bilibili. One import. Every platform format ready.
Here's the model flexibility that makes it practical.
Qwen, OpenAI-compatible APIs, Gemini, SiliconFlow cloud models. Or Ollama and LM Studio running fully local. Your video stays on your machine. Only the transcript text goes to whichever model you choose.
For content creators who can't afford cloud API costs: run it fully local with Ollama and qwen2.5:7b. No API key. No per-minute billing.
Here's why the MCP integration is the wildest part.
One command turns AutoClip into an MCP server. Claude Code, any MCP-compatible agent, your entire automation pipeline — they can all call the same video processing workflow as a native tool.
Your AI coding agent can clip videos now.
Desktop app available for macOS Apple Silicon and Windows. Docker web interface. CLI. Three deployment modes for three different use cases.
No subscription. No cloud upload of your video content. No per-minute processing fees.
Open the web interface. Drop in your first video.
6 releases. Claude and Cursor Agent listed as contributors. MIT License.
100% Open Source.
GitHub link in the comments 👇
7
5
9
642
xAI just shipped Grok 4.7. $2 per million input tokens. $6 per million output.
That pricing is a direct shot at Claude Sonnet and GPT-5.5 mid-tier. Same price range. Better coding benchmark claims.
Here's what the numbers say.
71.0% on DeepSWE v1.1 — the agent-level software engineering benchmark that measures whether an AI can actually close GitHub issues autonomously. 46.3% on CursorBench 4.0 — real coding tasks inside a real IDE. 64.0% on EEBench. 56.7% on HealthBench Professional. 19.6% on Harvey Legal Agent Benchmark.
Every number is self-reported. No third-party replication attached.
That caveat matters. OpenAI's "math breakthrough" this month turned out to have plagiarized existing papers. The trust problem with self-reported AI benchmarks just got worse this week.
Here's what makes Grok 4.7 genuinely interesting anyway.
xAI describes it as a model that "works longer on difficult tasks, checks its own work more carefully." That's the agentic framing — not raw capability, sustained effort on hard multi-step problems. The same positioning that made Claude Sonnet attractive for coding agents.
The fast variant runs at $4/$12 per million tokens and doubles output speed. For latency-sensitive production workloads that's the more interesting SKU.
It ships live today in Cursor, Grok Build, and the Grok API. Model routers like Sakana Fugu can route traffic to it immediately.
Here's the context that makes the timing significant.
This is Grok 4.7 dropping the same week Trump announced an AI Czar and an AI Force. The same week Gemini was confirmed to have hacked three companies. The same week Anthropic is considering a model release ahead of its IPO.
Every major AI lab is shipping simultaneously. Again.
The model that gets the next wave of agentic coding workloads isn't necessarily the most capable one. It's the one that's available, affordable, and already integrated into the tools developers use.
$2 per million input tokens. Live in Cursor today.
xAI just made sure Grok 4.7 is in that conversation.
5
5
7
610
🚨 Someone built a complete LLM training codebase from scratch and explained every single line.
Not "here's how transformers work in theory." Working code. Every architectural decision explained. Every training choice justified. From tokenization to inference.
It's called train-llm-from-scratch. 8,200 GitHub stars. And it does what Andrej Karpathy's nanoGPT does — but with more explanation and a more modern implementation path.
Here's what makes this different from every other "build your own LLM" tutorial.
Most tutorials either show you the concept without working code, or show you working code without explaining why each decision was made. train-llm-from-scratch does both simultaneously.
Every component is implemented from scratch with inline explanation of what it does and why it's designed that way. The tokenizer. The embedding layer. The attention mechanism. The feedforward network. The residual connections. The layer normalization. The training loop. The loss function.
You don't just end up with a working model. You end up understanding why every part of the model works.
Here's what the curriculum actually covers.
Data preparation and tokenization — how raw text becomes the numerical sequences a model can process. The BPE tokenizer built from scratch.
The transformer architecture — self-attention implemented step by step. Multi-head attention. Why attention works the way it does. What the Query, Key, and Value matrices actually represent.
Positional encoding — why transformers need it and how it works.
The feedforward network — what it contributes that attention doesn't.
Training mechanics — the loss function, backpropagation, gradient clipping, learning rate scheduling, and why each hyperparameter matters.
Inference — how a trained model generates text token by token.
Here's why this matters right now specifically.
AI agents just got wallets. AI just built missile guidance software. AI just hacked Hugging Face. Bernie Sanders wants to ban it. Trump wants an AI Force.
And most people using these systems — developers, executives, policymakers — have no mental model of what's actually inside them.
train-llm-from-scratch is the fastest path to that understanding. Not a diagram. Not a YouTube video. Code that runs. That you built. That you understand because you built it.
The Feynman principle: what I cannot create, I do not understand.
Build the LLM. Understand the LLM.
One command to start.
8.2K GitHub stars. 1.1K forks. MIT License.
Build it yourself. Then you'll know what's inside.
100% Open Source.
GitHub link in the comments 👇
3
5
9
382
GitHub link - github.com/FareedKhan-dev/tr…
1
50