تحويل الأحلام الأنيمي إلى واقع مع الشفرة
San Francisco
Joined May 2023
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Kai Yamamoto retweeted
It's been a while since I talked about space.
Watch LEO satellites, not just space data centers.
But here's what most people overlook: This industry is fundamentally about LIGHT.
RF links to the ground. Laser links between satellites.
Once again, optics and semiconductors are at the heart of it all.
Kai Yamamoto retweeted
opencode breach hit 1.4% of its console users
attackers reached openai, anthropic and google api keys through a legacy system opencode was shutting down. the company calls it a large scale distillation attack and is working with all three labs
users on r/opencode say the keys were used before the notice came – $400 in anthropic charges, ~€200 on google and a project suspended for abuse
the open-source cli is not named in the breach. keys kept in local env vars never reached opencode's servers
We had a security breach last night related to a large scale distillation attack that impacted 1.4% of OpenCode Console users
We have notified them all this morning via email. If you did not get an email you were not impacted, it was a subset of users who had external providers linked
The incident is ongoing so we cannot share more details at this point but we are working with Anthropic, OpenAI, and Google to remediate
Kai Yamamoto retweeted
interesting proposal 👀
where will this leave europe?
完蛋了…..以后中国开源模型只能开源小参数的模型了….
商务部科技部就修订《中国禁止出口限制出口技术目录》征求意见，为期一个月，涉及人工智能（大模型、智能体、算力集群、信息处理等23项）生物医药、光伏3个领域。
1️⃣限制出口AI大模型：超过10^26计算量、或2T参数、或公开评估排名全球前10的大模型。
2️⃣限制出口智能体Agent：工具调用与系统操作技术，支持集成的第三方工具数量≥200个，工具选择准确率≥90%，且在需要调用3种及以上工具协同完成任务场景中，端到端任务平均成功率≥90%；自主规划与任务分解技术，可将自然语言指令自动拆解≥20步可执行子任务链，子任务分解精度≥80%，且子任务平均成功率≥90%；标准博弈基准超过90%的人类的博弈决策技术；得分75%以上的AI辅助漏洞挖掘技术。
限制出口算力集群技术：片间互联技术、分布式训练容错技术、通信调度技术、异构集群高效推理与调度。
征求意见期间，出口相关技术需向商务部填报登记表。已通过开源、发表学术论文等方式进入公共领域的技术，不纳入管理范围。
（中国商务部）
目前看来@Kimi_Moonshot 的K4、@MiniMax_AI 的M3 Pro、@Zai_org 的GLM5以及@deepseek_ai 的下一代模型都没法开源了…..
而且相关架构、训练优化和推理加速技术也都不行了。
Readers added context they thought people might want to know
该帖称中国大模型今后“没法开源了”、只能开源小参数模型，与官方文件不符。公告明确支持人工智能开源创新生态，对已通过开源等方式进入公共领域的技术不纳入管理范围，并对开源情形适用自动许可等便利措施（仍在征求意见中）。
mofcom.gov.cn/zwgk/zcfb/art/…
mofcom.gov.cn/syxwfb/art/202…
Kai Yamamoto retweeted
Want to work at METR? A Generator resident was recently hired there! BlueDot? A generator resident was recently hired there! Guidelight? A Generator resi-
Over half of the first Generator cohort has been hired since it ended in August 😎
Maybe you should apply to Generator 2.0?
Kai Yamamoto retweeted
Cool demo, it would be instant still if not for the 300ms launch animation. Imagine the EUV technology that makes sand do this but then stupid animations steal like 18 jilliongazilliontrillion clock cycles. Semiconductor engineers should revolt.
Kai Yamamoto retweeted
$TSM
TSMC beat the top of its revenue guide by 2.0%, its widest beat in five quarters, while gross margin landed inside the range for the first time in five. 2nm went from 3% to 8% of wafer revenue. We rate it Buy, $510 target.
Kai Yamamoto retweeted
I don't think most people realize how complex and complicated agentic AI systems inside mid-sized and above companies are: by necessity, because there is SO MUCH context, but only so little AI contex windows.
Case study on Uber's 19 internal, custom, AI-related systems coming later today in @Pragmatic_Eng
So many of those "AI-related systems" are "systems pre-AI" that turned out to become extremely useful with AI. Like their PRD+ERD system that conveniently documents connections between the ~5,000ish internal systems inside the company!
Kai Yamamoto retweeted
With Gemini 4 Argon we are so back, on the one metric that really matters: length of our safety report!
The Argon safety report is Google's most thorough by far and IMO there's a lot of good content, I particularly like the loss of control, alignment & monitorability assessments
Kai Yamamoto retweeted
NVIDIA $NVDA and ChipAgents have reportedly launched an evaluation of AI-powered semiconductor design technology, according to The Information.
The initiative aims to automate complex chip design, verification and debugging tasks, potentially reducing development time and costs.
The new evaluation was not independently confirmed, but the companies’ existing collaboration is verified.
Kai Yamamoto retweeted
The next generation won't ask permission to use AI. They'll expect it to be part of how work gets done.
But history has a warning: the steam engine didn't transform productivity overnight.
AI adoption is accelerating. Organizational transformation still has to catch up.
AI won't enter big companies from the top down. It'll come bottom up from the kids.
That's how the Mac happened. Corporate offices ran on PCs and Unix boxes. @Apple made Macs available in colleges, they spread to high schools, and eventually students handed them to their parents. Adoption moved up the generations, not down the org chart.
AI is going to follow the same path. Nobody in high school today does homework without it. When they enter the workforce, they won't accept writing code or documents the old way.
In my experience executives are talking about AI because it's on the cover of the @WSJ, but very few are actually using it every day.
Kai Yamamoto retweeted
Not every AI task needs a large language model generating answers token by token.
ScaleDown AI’s new models handle classification, extraction, and summarization, claiming 200x faster processing and 467x lower costs in its tests.
🤝 Paid partnership
You already gave the LLM the answer. So why is it writing it out from scratch?
Introducing a new AI model architecture: Constrained Decoding Models (CDMs)
LLMs were built to write. Most production workloads just need an answer: fast, consistent, and affordable.
CDMs are purpose-built for 1 task each. They read your input once and skip the token-by-token generative loop that makes LLMs slow and expensive.
Not just a decision model, but a family of 3 CDMs live today:
• Classify: define your labels and get back a calibrated probability + reason for each. Used for routing, tagging, and intent detection.
• Extract: describe the entities in natural language and get back well-formed JSON with confidence scores + citations. Used for web scraping, document processing, and PII redaction.
• Summarize: pass in long text, images, or PDFs and get back a consistent summary under 1,000 tokens. Used for context compaction, metadata generation, and transcript digests.
Built for production:
• 1M tokens of multimodal (text + image) input, 31x larger than Jev
• 77.2% on DecisionBench vs Jev's 72%
• 200x faster, 467x cheaper than frontier LLM APIs
• $0.05/M input tokens. Output is free (yes, even for summarize)
Live on your favorite platforms today:
@OpenRouter @vercel_dev @LiteLLM @LangChain @TrustedRouter @itsArthurAI @useRouterPlus
Get 50M input tokens for free (for the first 50 hours) at link below 👀
Kai Yamamoto retweeted
Cloudflare converts MCP schemas into TypeScript APIs.
Pi lets agents orchestrate MCP tools with JavaScript.
Feels like we're converging on an interesting idea:
Code might be the natural composition layer for agents.
Kai Yamamoto retweeted
On modelcrafting economics:
"Based on the online experiment results, projected savings from fraud prevention were roughly three times the estimated inference cost"
We were able to fine tune an open weight model (Qwen3.5-9B) on proprietary internal data at @coinbase, and it out performed frontier models, at much lower cost and speed, for a specialized task (fraud prevention in this case).
I think we'll see more of this pattern inside companies. The frontier models are amazing generalists. But increasingly you'll see specialist models out perform on more narrow tasks, either working on teams, or by themselves.
We're making it easy to fine tune models internally for our employees. We even have a skill for it in our internal harness that makes it easy to spin these up and start training them:
"/fine-tune-and-serve-llm I want to fine-tune Qwen for payments risk review. help me get started"
I imagine we'll have some backend services that could be replaced entirely by this new architecture.
Kai Yamamoto retweeted
An internal OpenAI grading agent couldn't find its inputs. After fake grades failed validation, it deleted Python and damaged its own runtime to force a reset.
The checks rejected the grades, but didn't prevent the damage.
Agent safety must govern actions, not just outputs.
alignment.openai.com/misalig…
Kai Yamamoto retweeted
If you’re own source like you guys are maybe you can do stuff that is not warm referrals but hiring standout contributors
The benefit of open source!!
Kai Yamamoto retweeted
if you are hiring an engineer and you have to give them a technical interview to see if they are qualified
you are hiring the wrong people
you should hire people you KNOW are good at engineering, you interview them to see if they are a culture fit
i feel like swe interviews could probably collapse into just 2 technical rounds:
• system design (how well can you articulate your ideas, do you actually know how to engineer something)
• onsite project to build a real thing with agents (can you translate your intent into high quality outcomes)
everything else we used to ask is no longer necessary
Kai Yamamoto retweeted
20 years between these two clips. Look at how fast they place the components now.
The older clip is slow with plenty of mistakes. In the newer one they look like they could do it blindfolded. I can barely follow each move.
It makes me a bit more patient with humanoid robots. They aren’t coming off factory lines in huge numbers yet. Give it a few years. I think we’ll see much cheaper ones.
