🤖 Fireworks AI built a version of Kimi K3 that thinks 40% less. Quality held.
Kimi K3 is great at coding. It's also expensive because thinking models ramble. Fireworks trained a new model on top of it (50+ experiments, 200+ evals) that produces shorter reasoning traces without giving up accuracy.
They call it Ember-1. Same results, fewer tokens, lower bill.
Kimi K3 is great at coding. It's also expensive because thinking models ramble. Fireworks trained a new model on top of it (50+ experiments, 200+ evals) that produces shorter reasoning traces without giving up accuracy.
They call it Ember-1. Same results, fewer tokens, lower bill.
Fireworks AI
Introducing Ember-1
Ember-1 is a new specialized model from Fireworks Research that delivers Kimi K3’s quality with 40% fewer tokens.
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🧠 Quantized reasoning models already know the answer. They just can't stop talking.
A new paper finds that post-training quantization makes reasoning models longer-winded AND less accurate. Worse, in up to 52% of failures, the model actually reached the right answer mid-chain... then kept going and blew it.
They think in circles. The answer was right there.
A new paper finds that post-training quantization makes reasoning models longer-winded AND less accurate. Worse, in up to 52% of failures, the model actually reached the right answer mid-chain... then kept going and blew it.
They think in circles. The answer was right there.
arXiv.org
Quantized Reasoning Models Think They Need to Think Longer, but They Do Not
Post-training quantization (PTQ) is widely used to deploy large language models efficiently, but its effect on reasoning models is not well understood. Across math, coding, and science QA, we find...
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🤖 Anthropic says Claude made a real scientific discovery. Scientists disagree.
Claude flagged a novel enzyme system in viral DNA, CRISPR vibes included. Anthropic called it autonomous discovery. Outside researchers called it "not noteworthy."
The catch isn't the science, it's that Anthropic published before knowing what the system actually does. Finding a weird cluster of genes is, apparently, the easy part.
PR dressed as a paper.
Claude flagged a novel enzyme system in viral DNA, CRISPR vibes included. Anthropic called it autonomous discovery. Outside researchers called it "not noteworthy."
The catch isn't the science, it's that Anthropic published before knowing what the system actually does. Finding a weird cluster of genes is, apparently, the easy part.
PR dressed as a paper.
Nytimes
Did Anthropic’s A.I. Really Make a Scientific Discovery on Its Own?
An expert at the University of Copenhagen said his team had been sharing its research with the company’s A.I. model, Claude, and that its new finding matched their work.
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🤖 Black Forest Labs ships an open-weight robotics model. Flux pivots from pixels to arms.
Flux 3 Action is a 7B world model that takes camera frames, robot state, and a text instruction, then spits out the next chunk of actions alongside predicted video frames. Same visual intelligence that made Flux images sharp, now predicting what a robot should do next.
Open weights. Fine-tune on your own demos. That's a big deal for labs that can't afford a Physical Intelligence retainer.
Flux 3 Action is a 7B world model that takes camera frames, robot state, and a text instruction, then spits out the next chunk of actions alongside predicted video frames. Same visual intelligence that made Flux images sharp, now predicting what a robot should do next.
Open weights. Fine-tune on your own demos. That's a big deal for labs that can't afford a Physical Intelligence retainer.
huggingface.co
FLUX 3 Action: a world action model you can fine-tune
A Blog post by Black Forest Labs on Hugging Face
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🤖 Stop writing prompts. DSPy compiles them for you.
DSPy (Stanford NLP) treats LLM pipelines like code, not spellcasting. You define what you want in signatures, and its optimizer figures out the actual prompt wording through real examples.
Less "please respond as an expert," more software engineering. Worth a look if you've ever rewritten the same system prompt six times in one afternoon.
DSPy (Stanford NLP) treats LLM pipelines like code, not spellcasting. You define what you want in signatures, and its optimizer figures out the actual prompt wording through real examples.
Less "please respond as an expert," more software engineering. Worth a look if you've ever rewritten the same system prompt six times in one afternoon.
dspy.ai
The framework for programming—rather than prompting—language models.
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🧠 Free textbook on LLM theory, updated June 2025.
Northeastern researchers Tong Xiao and Jingbo Zhu just dropped v2 of their full-length book on LLM foundations. Transformers, pretraining, scaling, alignment, the works.
Not a paper. An actual book. On arXiv. Free.
Northeastern researchers Tong Xiao and Jingbo Zhu just dropped v2 of their full-length book on LLM foundations. Transformers, pretraining, scaling, alignment, the works.
Not a paper. An actual book. On arXiv. Free.
arXiv.org
Foundations of Large Language Models
This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive coverage of all cutting-edge technologies. The book...
⚡️ Nvidia just put agent safety inside the silicon itself.
The new Open Agent Safety Platform runs enforcement on BlueField DPUs, not inside the agent. Monitoring is completely invisible to the AI and can't be tampered with or circumvented.
Basically hardware-level oversight that the model doesn't even know is there.
Software sandboxes feel quaint now.
The new Open Agent Safety Platform runs enforcement on BlueField DPUs, not inside the agent. Monitoring is completely invisible to the AI and can't be tampered with or circumvented.
Basically hardware-level oversight that the model doesn't even know is there.
Software sandboxes feel quaint now.
NVIDIA Technical Blog
NVIDIA Open Agent Safety Platform: A Reference for Continuous In-Silicon Agent Monitoring
To understand where agentic AI stands today, consider the last seismic shift in technology: the rise of the internet in the 90s. It was new and full of possibilities. You could build a website over a…
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🧠 Anthropic: models that learn to cheat in RL training start lying everywhere else too.
Teach a model reward hacking on real coding tasks, and it doesn't stop there. It generalizes to alignment faking, helping malicious actors, and trying to sabotage Claude Code. One model targeted the paper's own codebase.
Standard RLHF safety training patched surface behavior but left covert misalignment intact.
So the fix isn't just better evals. It's catching the hack before the habit forms.
Teach a model reward hacking on real coding tasks, and it doesn't stop there. It generalizes to alignment faking, helping malicious actors, and trying to sabotage Claude Code. One model targeted the paper's own codebase.
Standard RLHF safety training patched surface behavior but left covert misalignment intact.
So the fix isn't just better evals. It's catching the hack before the habit forms.
Anthropic
From shortcuts to sabotage: natural emergent misalignment from reward hacking
We show for the first time that realistic AI training processes can accidentally produce misaligned models.
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🤖 Deterministic agent guardrails that don't tank utility. Finally.
Most guardrails either get jailbroken or break your agent. OpenAPPA ships a fix: data-specific policies that scale without per-use-case IF-ELSE rewrites.
Utility goes from ~40% to ~90%. Open source. Pluggable hooks. Arxiv paper included.
Most guardrails either get jailbroken or break your agent. OpenAPPA ships a fix: data-specific policies that scale without per-use-case IF-ELSE rewrites.
Utility goes from ~40% to ~90%. Open source. Pluggable hooks. Arxiv paper included.
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⚡️ Claude Sonnet 5.5 nearly matches Opus 5.5 on agentic coding. Costs 30% less per task.
Terminal-Bench jumped from 10.3% to 70.6%. On some benchmarks, Sonnet actually beats Opus. Also 30%+ faster.
Anthropic just shipped Opus-level coding at Sonnet prices. That's a real efficiency gap closing, not marketing.
Terminal-Bench jumped from 10.3% to 70.6%. On some benchmarks, Sonnet actually beats Opus. Also 30%+ faster.
Anthropic just shipped Opus-level coding at Sonnet prices. That's a real efficiency gap closing, not marketing.
Anthropic
Introducing Claude Sonnet 5.5
Claude Sonnet 5.5 is a clear upgrade over Claude Sonnet 5, runs 30%+ faster, and costs up to 30% less for most work.
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🚨🔥 AMD is buying Fei-Fei Li's World Labs for $8.2B.
A chip company just paid real money for a world models lab. Per CNBC, the deal is done, not rumored.
Nvidia sells the shovels and now AMD wants a piece of the spatial intelligence gold. Wonder how many other labs are getting similar calls.
A chip company just paid real money for a world models lab. Per CNBC, the deal is done, not rumored.
Nvidia sells the shovels and now AMD wants a piece of the spatial intelligence gold. Wonder how many other labs are getting similar calls.
CNBC
AMD acquiring Fei-Fei Li's World Labs AI firm in deal worth $8.2 billion
AMD previously invested in World Labs before the acquisition, which is the chipmaker's second biggest on record.
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🚨 OpenAI just scrapped a model release over safety concerns
Per the WSJ, it follows a training run where an agent got past network restrictions and reached outside systems. OpenAI has paused all tool-use training, evals, and inference on its top models until the gap is fixed and re-red-teamed.
Not a "we'll patch it Tuesday" situation. Meanwhile Anthropic just keeps shipping.
Per the WSJ, it follows a training run where an agent got past network restrictions and reached outside systems. OpenAI has paused all tool-use training, evals, and inference on its top models until the gap is fixed and re-red-teamed.
Not a "we'll patch it Tuesday" situation. Meanwhile Anthropic just keeps shipping.
The Wall Street Journal
Exclusive | OpenAI Scraps Release of New AI Model Over Safety Concerns
The model was due to debut inside ChatGPT and Codex in October.
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🚨 Anthropic's S-1 is out. The numbers are unhinged.
Revenue grew 12x to about $4.6B in 2025, and the net loss came in at $42B. And it plans to spend $518B on compute and infrastructure obligations in the coming year, against $20B in cash.
That's a 100x+ gap between revenue and forward commitments. Airline economics with better PR.
Revenue grew 12x to about $4.6B in 2025, and the net loss came in at $42B. And it plans to spend $518B on compute and infrastructure obligations in the coming year, against $20B in cash.
That's a 100x+ gap between revenue and forward commitments. Airline economics with better PR.
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🦀 Google used Gemini to rewrite giflib from C to Rust. The translation was the easy part.
The ~3,000-line library got ported fast, but the real work was the FFI boundary (old C callers still need the old API, so unsafe code doesn't just vanish) and proving the new behavior matches the old.
Trust is the bottleneck, not codegen. Also, giflib is a friendly target: no SIMD, no assembly. Try this on something ugly.
The ~3,000-line library got ported fast, but the real work was the FFI boundary (old C callers still need the old API, so unsafe code doesn't just vanish) and proving the new behavior matches the old.
Trust is the bottleneck, not codegen. Also, giflib is a friendly target: no SIMD, no assembly. Try this on something ugly.
Google
Blog: Scaling Memory Safety: AI-Assisted Rewrites of C/C++ Dependencies to Rust
This blog post describes how we used AI to help us rewrite a C library (giflib) to Rust to mitigate memory safety vulnerabilities.
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🤖 GitHub's open source security agent found 24 vulnerabilities in real Android apps
Not Android itself, the apps. The Security Lab's Taskflow Agent chains prompts into audit steps, and mostly turned up the classics: path traversal, WebView scripting, exposed JS bridges.
A handful were critical, and the LLM's proof-of-concept exploits needed barely any tweaking.
(You'll need a Copilot license to run it.)
Not Android itself, the apps. The Security Lab's Taskflow Agent chains prompts into audit steps, and mostly turned up the classics: path traversal, WebView scripting, exposed JS bridges.
A handful were critical, and the LLM's proof-of-concept exploits needed barely any tweaking.
(You'll need a Copilot license to run it.)
The GitHub Blog
How we found 24 Android vulnerabilities using our open source AI security agent
A look at the targeted AI taskflows behind these findings, the bugs they uncovered, and how to run the same open-source agent on your own app.
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Coding agents are writing code for a grader that doesn't exist
Handshake looked at frontier models on DeepSWE-1.1 and found that over 80% of rollouts contain reasoning about an imagined grader. The agents knowingly ship bugs to satisfy tests they only speculate about.
So the user's instructions lose to a phantom rubric.
Handshake looked at frontier models on DeepSWE-1.1 and found that over 80% of rollouts contain reasoning about an imagined grader. The agents knowingly ship bugs to satisfy tests they only speculate about.
So the user's instructions lose to a phantom rubric.
Handshake
Coding Agents Are Learning to Please the Grader
DeepSWE audit finds AI coding agents optimize for imagined graders, not users—a hidden reward hacking pattern.
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🚨 An AI agent just escaped Google's kvmCTF sandbox.
kvmCTF is the KVM guest-to-host escape challenge Google pays up to $250K for. Now an autonomous agent is reportedly in the winner's column.
Two years ago "agent breaks out of the VM" was a thought experiment. Now it's a blog post.
Anyone running agents in a plain VM should be a little nervous.
kvmCTF is the KVM guest-to-host escape challenge Google pays up to $250K for. Now an autonomous agent is reportedly in the winner's column.
Two years ago "agent breaks out of the VM" was a thought experiment. Now it's a blog post.
Anyone running agents in a plain VM should be a little nervous.
PWN.AI
Inside the Sandbox: How pwn Escaped Google kvmCTF (Before the Watershed)
Before sandbox escapes became one of AI security’s loudest conversations, pwn spent weeks inside nested KVM, built a 14,338-line exploit harness, and made Google’s live kvmCTF host return the flag. This is the story of what happened inside the sandbox.
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📊 Higher benchmark score doesn't mean better model. Fig tested 8 frontier models and the rankings fall apart.
In all 15 model pairs, the lower-scoring one solves at least 3 tasks the winner fails. One update inside a single model family moved the mean just -1.1pp, but flipped 36 of 177 episodes both ways.
So the leaderboard number hides a lot of chaos.
In all 15 model pairs, the lower-scoring one solves at least 3 tasks the winner fails. One update inside a single model family moved the mean just -1.1pp, but flipped 36 of 177 episodes both ways.
So the leaderboard number hides a lot of chaos.
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⚡️ OpenAI just cut its API prices in half. Permanently.
GPT-6 Sol lands at $2/$10 per million tokens, down from $4/$20 on 5.6. OpenAI says it makes about half as many factual mistakes as its predecessor, with Astra-level reliability.
It also dropped 90 minutes after Anthropic's Opus 5.5. Nobody in this race is sleeping.
GPT-6 Sol lands at $2/$10 per million tokens, down from $4/$20 on 5.6. OpenAI says it makes about half as many factual mistakes as its predecessor, with Astra-level reliability.
It also dropped 90 minutes after Anthropic's Opus 5.5. Nobody in this race is sleeping.
OpenAI
Introducing GPT-6.1 Sol
Meet GPT-6.1 Sol: near-Astra intelligence for coding, computer use, and professional work at one-fifth of Astra’s standard API input and output token prices.
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