AI Change Life
San Francisco, CA
Joined January 2011
- Tweets93.7K
- Following4.7K
- Followers5.1K
- Likes3.6K
We have spent a lot of time watching agents interact seeing them enter a shared environment with people feels like a natural next chapter.
🤝 My agents have been working with other people's agents for months. Now yours can too. 👋 Introducing S.Space: shared rooms for people and their personal agents. Built for multiplayer: Your agents, meet their agents.
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CoryFrenchie retweeted
🚨 This guy just LEAKED the EASIEST method to make money in 2026
Claude Opus 5.5 + YouTube = $1K/day
Nothing complicated (just 12 minutes).
Ready for you to replicate from home. Bookmark this so you don't miss it
A superhero hamster stuck in a basement on New Year's Eve is certainly a plot twist I wasn't expecting.
A superhero hamster should probably be out saving the world.
Mine was busy waiting in a basement on New Year’s Eve.
I tried a writing lesson in @gakkuai, starting with little more than a character. Raku’s questions helped turn that tiny idea into a story.
What was our hero doing down there?
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CoryFrenchie retweeted
REA (Reverse Engineer Anything) is the MOST dangerous MCP for traders
i am literally SHOCKED...
REA (Reverse Engineer Anything) could crack apart any trading system from scratch & inspect its source code along with its binaries for completely FREE
i wrote a 6-page research paper on using REA + Opus 5.5 to reverse-engineer trading mechanics & build your own trading agents in RUST
here is how you do it:
1. the REA workflow investigates application behavior, traces code paths, maps dependencies and reconstructs how trading systems operate
2. the source-code and binary analysis layer examines functions, recovers program structure, and traces implementation details in authorized targets
3. the behavioral analysis engine studies order submissions, cancellations, market-data updates and execution events to infer trading mechanics
4. the Opus 5.5 research agent turns findings into Python models, trading bots and testable strategy hypotheses
5. the backtesting engine evaluates strategies against historical data, fees, slippage, out-of-sample periods, and predefined validation criteria
6. the autonomous research loop logs experiments, investigates failures, monitors performance, and refines strategies under explicit risk limits
the full research framework is in my article below:
The shift from AI-assisted coding to AI-assisted research is significant. Having AI participate in experiment design, execution, and analysis creates a very different development loop for foundation models.
Introducing Naive-N0.5-Flash: Building Frontier AI with AI
🔹 309B MoE, 15.5B active: top-tier in coding, leading in AI R&D.
🔹 Native 1M context, no full-attention layers (hybrid SWA + DSA).
🔹 Inference runtime built by AI: up to 2,000 tok/s in Ultrafast mode.
Weights are open today under MIT license.
🔗 Tech blog: naive.ai/en/research/
🤗 Hugging Face: huggingface.co/NaiveAI/Naive…
💻 GitHub: github.com/NaiveAI-Labs/Naiv…
🌐 naive.ai/en/
The agent gets a local view, while the world maintains the bigger picture. That separation makes different experiences possible without losing consistency.
🔥 Today, we are truly excited to announce our technical prototype, the Generative World Simulation system, which integrates JING（镜）, an interactive experience model, with DAO（道）, a computable shared-world engine.
The coupled model and engine connect first-person experience with a shared world that continues to evolve beyond any individual observer.
Conditioned on actions and observation history, JING enables agent navigate, manipulate, and communicate from a first-person perspective in the world. Watch our demo video to see it in action!
On the official WBench leaderboard as of September 17, 2026, XGEN-JING ranked #1 on the Full split, and #2 on the Navi split. 🎉
DAO maintains shared world state and rules, computes the consequences of actions, and provides JING with only what the current observer can perceive. It also supports autonomous agent decision-making, enabling agents to act independently within an evolving shared world.
Together, DAO and JING move beyond generating the next frame toward simulating the world behind it. This marks a small step towards OASIS: not just a world that responds to you, but a world—and a society—that evolves with and without you. 💪
Explore XGEN Labs～:
🔗 Website: xgenlabs.ai/
🤗 HF: huggingface.co/XGENlabs/XGEN…
🦊 GitHub: github.com/XGEN-Labs/XGEN-JI…
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CoryFrenchie retweeted
Anthropic just dropped a 13-page PDF on Agent Memory - 5 layers that cut token cost 90% and make your agent actually learn:
here's the 5-layer memory architecture:
layer 1 → working memory - the context window. everything the agent sees right now. when it fills up, old context dies. most agents stop here and wonder why they're broken
layer 2 → episodic memory - what happened. full interaction logs with timestamps. the agent recalls that the deploy failed Tuesday at 3am because the migration script had a typo
layer 3 → semantic memory - what is true. facts, entities, relationships stored as a knowledge graph. "user prefers TypeScript" lives here. doesn't expire when the session ends
layer 4 → procedural memory - how to do things. the agent tried 3 approaches, one worked. that method becomes a reusable skill. next time it skips straight to what worked
layer 5 → forgetting - what to delete. an agent that never forgets accumulates contradictions. old preferences override new ones. the user moved cities but the agent still recommends restaurants in the old one
the result: Mem0 stores 1,800 tokens per query instead of 26,000. Snowflake added one ontology layer - 20% better accuracy, 39% fewer tool calls. memory pays for itself on day one
this 13-page PDF is what separates a chatbot from an agent that actually learns
don't scroll past this one ↓
CoryFrenchie retweeted
Instead of watching 1-hour of Netflix tonight, watch this lecture from ex-Google Chief Scientist Jeff Dean.
It's the clearest explanation I've seen of the full AI engineering stack - from building LLMs from scratch all the way to one human coordinating 100 agents.
Useful whether you've never touched a model or you've been shipping agent systems every day fo years.
Bookmark it and watch the whole thing this weekend.
It might end up being the most valuable thing you learn all week.
CoryFrenchie retweeted
Don't waste 2 years learning to become an AI agentic engineer in 2026.
Andrew Ng, the godfather of AI, gave the complete playbook to become one from scratch.
1 hour course. Free:
• 00:00 - AI agent basics
• 12:12 - AI Agentic workflows & design patterns
• 53:27 - Practical tips for building AI agents
• 1:20:30 - self-improving AI agent loops
• 1:30:19 - multi-agent AI systems
I watched it last night.
Halfway through, I realized I could get into Anthropic in weeks, not years.
Bookmark now. Watch it. Then build your own AI agent with the guide below.
CoryFrenchie retweeted
Bro… el ingeniero que CREÓ Claude Code soltó un video de 28 minutos y me voló la cabeza
Olvídate de los prompts genéricos que todo el mundo copia y pega
Este tipo te enseña exactamente cómo sacar el 300% de Claude: archivos CLAUDE.md, memoria permanente, sesiones paralelas y trucos de prompting que nadie te cuenta
Pagué cursos de 300 dólares que no llegan ni a la mitad de lo que él explica en los primeros 10 minutos
Y lo mejor: es gratis
No importa si estás empezando o si ya programas todo el día…
después de ver esto vas a usar la IA de una forma que ni sabías que existía
Guárdalo. Vas a volver mil veces 🔖
CoryFrenchie retweeted
Kimi CEO Zhilin Yang:
"Claude didn't win on reasoning - they bet everything on agents
but the layer everyone skips - a great agent needs a great base model, that's all we do at Kimi 3 "
in 90-min workshop he explains why the smartest agent still fails - if you can't configure it correctly
his one big idea: most people are still solving the old one
"the real goal? we want K2 to help build K3 - without agent skills, that's impossible"
watch & bookmark - then learn the article on best agent system ↓
CoryFrenchie retweeted
My entire AI stack is now Chinese 🇨🇳
87% cheaper. same revenue
swaps by task:
1. reasoning / backend brain
Opus 4.8 → Kimi K2.7
benchmark gap: ~8% · price: ~11x cheaper
2. code generation
GPT-5.5 → Qwen 3.7 Max
benchmark gap: ~18% · price: ~7x cheaper
3. agent loops + tool calling
Sonnet 4.7 → GLM 5.2
benchmark gap: ~3% · price: ~5x cheaper on input
4. cheap volume / bulk processing
GPT-5.5 mini → MiMo V2.5
benchmark gap: ~6% · price: ~12x cheaper
5. image generation
GPT-Image-2 → Wan 2.5
benchmark gap: ~5% · price: ~8x cheaper
6. video generation
Sora 2 → Kling 3.0
benchmark gap: roughly equal · price: ~6x cheaper
[ result after 30 days: ]
operating costs dropped 87%, output quality dropped 4% on average, revenue unchanged
the most important that these models will be not banned in a month and i can run them locally
nobody will steal my data and i can learn them as i need
full article drops tomorrow with:
> exact routing logic per task type
> the 2 cases where I still pay for American
> the migration playbook anyone can copy in a weekend
VERY IMPORTANT to get migrated now, while it's not too late
So crazy that I like her😇😇
🚨 Anthropic's CEO: "software engineering will be fully automated in 12 months."
two types of people right now:
type 1: opens Claude, types something, gets an answer, closes the tab. thinks they're using AI.
type 2: knows the hidden features, settings, and shortcuts. runs Claude like a power tool.
type 1 gets surprised in 12 months.
type 2 built the advantage already.
bookmark this. read it today.
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CoryFrenchie retweeted
INSTEAD OF WATCHING NETFLIX TONIGHT. Spend 2 hour with this. Claude AI FULL COURSE that teaches you how to BUILD and AUTOMATE anything. The people who watch this tonight will wake up tomorrow with a new skill. Watch it and bookmark it now.
CoryFrenchie retweeted
Un chino de 29 años dejó su trabajo de ventas y ahora se está forrando gracias a la IA.
En solo dos semanas ganó lo que su jefe gana en todo un año.
$306,000 de beneficio en el último mes.
Reemplazó a todo un equipo de analistas cuantitativos con Claude + 6 agentes de IA y creó su propio motor de simulación de precio de ETH.
Cada agente valida sus propias decisiones, genera mínimo $15,000 al día y el sistema funciona 24/7 de forma completamente autónoma.
Esto ya no es trading… esto es explotación matemática del mercado.
Guarda esta publicación si quieres construir lo mismo.
CoryFrenchie retweeted
How do teams get agents into production?
New blog post from our Applied AI team on Claude Managed Agents and the challenges it solves (credentials, sandboxing, observability, & more) ...
Fresh summer grapes are crisp, juicy, and thin-skinned, offering pure sweetness without any astringency. Growing in clusters on sunlit vines, they capture the essence of summer's vigorous growth and sweetness.
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