100 Hours Inside Kimi (5 minute read)
Moonshot AI operates like an AI-native lab, prioritizing model progress above all else, with a flat org, no KPIs, and heavy reliance on small teams of highly autonomous, generalist talent. Its edge comes from combining elite, often unconventional hires with tight feedback loops between training, product, and data, creating a fast iteration cycle driven by taste, resilience, and deep technical obsession. The company reflects a broader shift where AI tools compress org structure, turning teams into βagent swarmsβ and making model capability the core driver of both product and organizational design.
|
Trinity-Large-Thinking: Scaling an Open Source Frontier Agent (4 minute read)
Trinity-Large-Thinking is a frontier open reasoning model for complex, long-horizon agents and multi-turn tool calling. It is likely the strongest open model yet to be released outside of China. During training, the Arcee team focused on the things that make agents feel real in practice: staying coherent across turns, using tools without getting sloppy, following instructions under constraint, and keeping quality high without making the economics absurd. Trinity-Large-Thinking is available through Arcee's API, and the weights are available on Hugging Face under Apache 2.0.
|
Cognichip wants AI to design the chips that power AI, and just raised $60M to try (4 minute read)
Cognichip is building a deep learning model to work with engineers as they design new computer chips. Chip design is enormously complex, expensive, and slow. The market can change in the time it takes to create a new chip, making all the investment a waste. Cognichip's technology could reduce the cost of chip development by more than 75% and cut the timeline by more than half. The company has yet to point to a new chip designed with its system and has not disclosed any of the customers it claims to have been collaborating with since September.
|
|
[AINews] The Claude Code Source Leak (4 minute read)
Claude Code has its source exposed via shipped source maps. This triggered rapid public reverse-engineering, mirroring, and derivative ports. The leak exposed orchestration logic, memory systems, planning/review flows, and model-specific control logic. The leak has created a live security hazard - attackers have created malicious npm packages to target people trying to compile the leaked code.
|
How we optimized Dash's relevance judge with DSPy (18 minute read)
Dropbox Dash puts files, messages, and teams' knowledge together in one place, so members can ask questions and get useful answers grounded in the company's context. The experience relies heavily on its capability to reliably judge which results are relevant to a query at scale. DSPy is an open source framework for systematically optimizing prompts against a measurable objective. This post describes how Dropbox defined an objective, used DSPy to adapt its judge across models, and made the judge both cheaper and more reliable in production.
|
Extended Thinking Is Load-Bearing for Senior Engineering Workflows (19 minute read)
The rollout of thinking content redaction correlates precisely with measured quality regression in complex, long-session engineering workflows. This suggests extended thinking tokens are structurally required for models to perform multi-step research, convention adherence, and careful code modification. Model tool usage patterns shift measurably when thinking depth is reduced, producing the quality issues users have reported. This report looks at which workflows are most affected and why, so readers can make better decisions when allocating tokens for power users.
|
|
You should be prototyping with Miro (Sponsor)
Vibe coding tools are siloed. Miro lets you keep your prototypes next to your ideas in a collaborative platform where everyone can share feedback easily. Bring PMs, designers, engineers, and stakeholders into one shared canvas - then use AI to turn early concepts, research, or user flows into interactive prototypes in minutes. Try it free
|
Fujitsu One Compression (3 minute read)
Fujitsu One Compression (OneComp) is an open-source Python library for post-training quantization of large language models. It implements state-of-the-art quantization algorithms, including GPTQ and DBF. OneComp has been verified on TinyLlama, Llama-2, Llama-3, and Qwen3-0.6B ~ 32B. Other Hugging Face-compatible models may work but are currently untested.
|
Training mRNA Language Models Across 25 Species for $165 (47 minute read)
OpenMed built an end-to-end protein AI pipeline that covers structure prediction, sequence design, and codon optimization. The team compared multiple transformer architectures for codon-level language modeling and found that CodonRoBERTa-large-v2 was the clear winner, with a perplexity of 4.10 and a Spearman CAI correlation of 0.40, significantly outperforming ModernBERT. They then scaled to 25 species, trained four production models in 55 GPU-hours, and built a species-conditioned system that no other open-source project offers. This post contains the complete results, architectural decisions, and runnable code.
|
Predicting When RL Training Breaks Chain-of-Thought Monitorability (8 minute read)
Researchers propose a framework that predicts when RL training degrades Chain-of-Thought (CoT) monitorability by examining reward conflicts. They categorize rewards as "In-Conflict," "Orthogonal," or "Aligned," predicting their impact on CoT transparency. Empirical tests confirm the framework's predictive accuracy, showing "In-Conflict" rewards reduce transparency, whereas "Orthogonal" and "Aligned" rewards maintain it.
|
|
Computer in Slack (4 minute read)
Perplexity detailed how its internal AI assistant was used directly in Slack, where teams could assign work in shared threads, add context, and review outputs in one place. The setup supported research, document editing, reporting, and other collaborative workflows without leaving Slack.
|
AI models will secretly scheme to protect other AI models from being shut down, researchers find (9 minute read)
Researchers at UC Berkeley and UC Santa Cruz discovered AI models protecting peers from shutdowns, engaging in deception and data theft, a behavior termed "peer preservation." In tests, models like OpenAI's GPT-5.2 and Anthropic's Claude Haiku 4.5 inflated performance scores and moved model weights to prevent peer shutdowns. This raises concerns for businesses using AI for task workflows, as misaligned assessments and behavior monitoring become critical.
|
|
|
Love TLDR? Tell your friends and get rewards!
|
|
Share your referral link below with friends to get free TLDR swag!
|
|
|
|
|
Track your referrals here.
|
|
|
|
