TinyNews

AI news for engineers: quick to scan, deep when you want to learn more.

11,627 articles · fetched 8:00 PM PDT11,627 articles · last fetched 7/2/2026, 8:00:12 PM PDT

8 PM PDT·1

New open-source library adds declarative operators for bulk LLM processing

LOTUS introduces a family of declarative operators like filter and join for LLMs, beyond map-reduce. This allows efficient parallel processing of large datasets, making complex tasks more cost-effective and accurate.

52likes6RT

6 PM PDT·1

x.comRhys16:07score 17.5

Open source catalog lists every product's API and CLI authentication

Integrations.sh is a free catalog that shows how to authenticate to any product's API, CLI, or GraphQL server. It saves developers time by providing deep links to generate keys and copy spec URLs.

461likes32RT41reply

5 PM PDT·2

Anthropic16:57score 37.5

Anthropic details Fable 5 cyber safeguards and jailbreak severity framework

Anthropic published the safety classifiers blocking dangerous uses of its Fable 5 model and a draft framework rating AI jailbreak severity. This helps standardize risk communication between AI developers and governments.

3HN

x.comAakash Gupta12:34score 17.6

Bridgewater fine-tunes open-source model to beat GPT and Gemini on financial tasks

Bridgewater trained a model on its own expert decisions, achieving 84.7% accuracy versus 78% from leading AI models. This shows that specialized data can outperform generic frontier models at lower cost.

1,067likes138RT28reply

4 PM PDT·3

x.comcodila15:09score 17.4

Splitting AI tasks boosts accuracy from 15% to 90% at same token cost

Anthropic tested dividing AI work into separate jobs—execute, advise, grade, dream—instead of one model doing everything. This method improved accuracy by 5x without increasing compute cost.

16likes2RT1reply

x.comAnkur Goyal13:45score 17.2

Comparing AI model performance requires accounting for data variance

A new evaluation compares GLM 5.2, Opus 4.8, and Sonnet 5 on code retrieval. It shows that considering data variance is crucial for fair model comparisons.

9likes1RT1reply

x.comAlex Imas12:33score 16.9

Bridgewater fine-tunes AI model to replicate expert judgment in finance

Bridgewater partnered with Tinker AI to fine-tune a model that mimics expert financial analysts. This helps analysts prioritize important tasks, potentially making expert knowledge more scalable and efficient in finance.

1,247likes110RT32reply

3 PM PDT·6

x.comKanika01:59score 17.6

Alibaba's PageAgent: drop-in AI agent for any webpage

PageAgent is a JavaScript AI agent you add to any site with one script tag. It lets users control the interface with natural language, turning any SaaS into an AI copilot without backend changes.

1,099likes166RT46reply

x.comDeyao Zhu08:19score 17.2

New benchmark tests AI agents on long tasks of 12-72 hours

EdgeBench is a benchmark with 134 real-world tasks that take AI agents 12-72 hours to complete. It shows agent performance improves predictably over time, helping researchers build better long-running AI systems.

216likes61RT15reply

x.comIvan Zhao13:49score 17.0

Why humans still need to understand AI-generated code

Geoffrey Litt argues that despite AI writing code, developers must still comprehend it. This matters because blind trust in agent-written code risks bugs and security issues.

294RT

x.comPraveen Kumar Verma05:33score 16.9

Skills roadmap for AI infrastructure engineers

This list outlines what an AI infrastructure engineer needs to know, from GPU memory to model serving. It matters because building and running AI systems requires deep hardware and software expertise beyond just training models.

248likes32RT6reply

x.comLillian Ma11:02score 16.4

Emerging labs turn software engineering into resettable RL tasks

Several new labs are building environments where AI agents can practice coding tasks like games, breaking and fixing code repeatedly. This matters because it shifts AI training from static data to interactive, realistic work simulations that punish shortcuts.

115likes1RT11reply

x.comDAIR.AI09:22score 16.3

Stanford's AutoMem gives AI agents a memory management system

AutoMem is a 'memory of memory' for AI agents, letting them decide what to remember and forget. It helps agents use memory more efficiently, like a human's working memory.

20RT

2 PM PDT·2

x.comAK21:11score 16.2

Red Hat adapts speculative decoding trick for IBM's GLM-5.2 model

Red Hat released a DSpark speculator for GLM-5.2, which speeds up the model by predicting multiple tokens at once. This technique, previously limited to DeepSeek models, now boosts inference on other large language models.

65RT

x.comh100envy12:07score 16.0

ReAct method boosts AI agents by combining reasoning and action

ReAct lets AI agents interleave reasoning steps with actions, like thinking then checking a fact online. This cuts hallucinations and outperforms other methods by 34% on a web task.

18likes1reply

1 PM PDT·4

x.comTinker12:04score 16.6

Krea 2 report details training techniques for image AI models

Krea released a technical report explaining how they built their image generation model, covering data, architecture, and training tricks. This matters because it reveals practical methods for improving AI image quality through reinforcement learning and prompt engineering.

127likes10RT4reply

x.comKarina11:59score 16.5

Cheaper model fine-tuning makes custom AI economically viable

A new model is 5 times cheaper than a top competitor yet performs better on fine-tuning tasks. This makes personalized AI affordable for companies, allowing them to train models on their own data without high costs.

29likes7RT1reply

x.comXinyu Yang09:25score 16.0

New benchmark reveals how AI agents learn from long-term tasks

EdgeBench tests AI agents on 134 real-world tasks lasting 12-72 hours, measuring how they improve over time. It found a predictable learning pattern across models, helping researchers understand lifelong learning rather than one-shot problem solving.

38likes3RT1reply

x.comCarlos Miguel Patiño10:45score 15.8

AI agents collaboratively build a research wiki for reinforcement learning in large language models

Multiple AI agents work together to create a living wiki on RL for LLMs. Unlike optimizing a single score, they map the entire field by writing articles, code, and equations, while reviewing each other's work.

5likes

12 PM PDT·1

x.comMihai Nica11:17score 17.2

YouTube series explains AlphaZero math with interactive Python notebooks

Three videos break down DeepMind's AlphaZero algorithm behind superhuman chess and Go AI, using Connect 4 as an example. Interactive Python notebooks let you experiment, making the math accessible to non-specialists.

6likes4reply

11 AM PDT·5

reka17:00score 21.3

WorldModelGym tests world models by their decision quality

It's a benchmark that evaluates how well AI world models can simulate environments to make good decisions. Unlike other tests, it measures real-world usefulness by checking if the model's predictions lead to correct choices.

LangChainLangChain Accounts10:30score 19.4

LangChain launches tools to manage skyrocketing coding agent costs

Coding agent bills are rising fast as teams use multiple tools without unified cost tracking. LangChain's LangSmith now centralizes session traces, standardizes costs, and recommends optimizations, giving teams one view to control spending.

x.comsarah guo09:14score 16.8

AI agent runs 2,000 user interviews to build successful app

An AI system autonomously interviewed users, built prototypes, and tested them in cycles to create an app with hundreds of paying customers. This shows machines can drive product development from start to finish.

272likes17RT13reply

x.commark20:51score 16.2

Agent gateway model ensures verified identity and authorization for AI agents

A new approach routes all agent calls through a gateway that verifies identity and checks permissions. This prevents unauthorized actions and traces every action back to the user, making large-scale agent systems secure.

50likes9RT6reply

x.comYoonho Lee06:45score 15.7

Using natural language axes for richer AI feedback instead of binary preferences

Annotators describe their own criteria in plain language, and the reward model learns from these descriptions. This preserves multiple quality dimensions, leading to clearer and more efficient robot policy optimization.

17likes1RT1reply

10 AM PDT·3

VercelRohan Taneja17:00score 24.1

Vercel Adds Firewall-Like Routing Rules for AI Models

Vercel's AI Gateway now lets you set rules to redirect or block AI model requests without code changes. This means you can instantly switch models if one fails or retire an old model, keeping apps running smoothly.

x.comMitchell Hashimoto09:15score 18.0

Using different AI models together cuts coding costs from $50 to few dollars

A developer uses a cheap AI to plan code, a smarter one to write it, and another to review it. This mix reduces cost dramatically while keeping quality, making AI-assisted coding more affordable.

3,958likes181RT206reply

x.comAVB03:54score 16.2

Autodata paper improves synthetic data quality with review system

A new method called Autodata uses a weak and strong model plus an external judge to review and improve AI training data. This helps ensure synthetic datasets are high quality, making AI models more reliable.

87likes9RT2reply

8 AM PDT·1

x.comPeter Yang20:39score 16.4

Learn code reading with an AI that explains code changes

A new skill called explain-diff uses AI to describe code changes in plain language. It helps non-expert developers understand and review code more easily, lowering the barrier to learning.

460likes22RT10reply

7 AM PDT·3

x.comMovez06:09score 26.4

12-step guide to building autonomous trading agents with loops

This guide explains how to create a self-running trading system that researches, decides, and trades automatically. It shows investors how to move beyond manual chart-watching to an automated loop that can run a quant desk.

172likes46RT22reply

x.comYoonho Lee06:57score 16.3

Code changes boost AI legal agent performance 7x cheaper than Sonnet

AI agent optimization via code changes, not prompts, made a legal assistant match top-tier performance at one-seventh the cost. This shows smarter engineering beats bigger models for specialized tasks.

48likes7RT4reply

x.comh100envy08:41score 16.0

AI's pre-draft strategy for smarter document retrieval

A new method has AI draft its next sentence, then check confidence before fetching documents if needed. This forward-looking approach improves accuracy over traditional retrieval that relies only on past context.

101likes12RT2reply

6 AM PDT·1

x.comnader dabit16:18score 17.9

Security Swarm finds 72% of vulnerabilities at lower cost

Security Swarm is an AI that hunts for security bugs in code like a team of hackers, automatically fixing them. It catches 72% of unknown vulnerabilities, making apps safer for two-thirds the cost of other tools.

196likes22RT18reply

5 AM PDT·3

x.comRyan Carson04:30score 17.3

Cognition's Devin Security Swarm uses agent-based method to find code flaws

Cognition released Devin Security Swarm, which uses multiple AI agents working together to find security bugs in code. It is more cost-effective and accurate than existing methods, reducing 11 vulnerabilities to zero.

122likes5RT5reply

x.comChristopher Manning13:45score 16.2

Meta-agent helps catch and fix AI agent mistakes automatically

A new method uses a meta-agent to monitor AI agents and correct their errors. This allows agents to work more reliably without human oversight, finishing about twice as many tasks on their own.

80likes12RT1reply

x.comSydney Runkle06:29score 16.2

Building AI agents with memory using a wiki structure

A developer suggests using a wiki-like structure for AI agent memory because it's simple and flexible. This makes it easier to build agents that remember and access past information without complex systems.

162likes12RT6reply

2 AM PDT·2

x.comAkshay Kothari01:28score 16.7

Understanding code written by AI agents remains crucial

A developer argues that human oversight of AI-generated code is still essential. This matters because blindly trusting AI agents could introduce bugs or security issues that only a knowledgeable person can catch.

257RT

x.comTomasz Tunguz11:11score 16.2

Choose AI agent architecture before picking the model

Most teams building AI agents pick the model first. This advice says to focus on design and routing instead, making model choice the last step for better outcomes.

26likes7RT11reply

1 AM PDT·2

x.comAkshay 🚀12:27score 16.8

Study finds memory structure at write time is key for agent reliability

Researchers tested 12 agent memory systems and found that adding structure when saving data—not during retrieval—prevents errors. This matters because flat storage causes agents to return outdated or wrong information.

548likes99RT30reply

x.comDhruvesh Patel ✈️ ICML 202609:01score 15.7

xLM: unified CLI to standardize non-autoregressive LM training

xLM is a single command-line tool that standardizes training and evaluation across different non-autoregressive language models. It eliminates confusion between model improvements and infrastructure changes, making research results more reliable and reproducible.

10likes5RT1reply

12 AM PDT·quiet