DevPick

Reco raised $55 million, bringing its total funding to $140 million, as competition intensifies in the AI agent security sector.

• •Numerous startups have entered the market for AI agent security.
• •Reco raised $55 million, following a $30 million round in February.
• •This funding brings Reco's total raised amount to $140 million.

Project Jevstiller, which distills Jev into local models, has been released, featuring disagreement bound assignment and benchmark verification.

• •Released in September 2026, the project uses a method to distill Jev into local models.
• •It features the ability to calculate and assign disagreement bounds between models.
• •Running benchmark scripts allows verifying numerical reproducibility without an API key.

Ahead of President Trump's meeting with tech CEOs, a senior Senate Democrat is calling for a US-China treaty to prevent global AI chaos.

• •President Trump is preparing to meet with CEOs of tech and AI companies in Washington.
• •A senior Senate Democrat is calling for a US-China treaty to prevent global AI chaos.
• •It has been pointed out that negotiations to prompt action from both countries' leaders may be difficult.

Anthropic's IPO preview documents highlight expanding losses and increased risks of model harm, as the company targets a valuation exceeding $2 trillion.

• •Reports indicate the IPO preview includes details on accumulating losses and proposals for management to retain authority.
• •The documents explicitly state that AI development plans could further increase the risk of models causing harm.
• •Anthropic is preparing for its long-awaited IPO with a target valuation of over $2 trillion.

A freedom of information request revealed that a six-month facial recognition trial at UK stations involved 500,000 scans but resulted in zero arrests and only one false positive.

• •The trial cost 320,000 pounds and consumed nearly 100 hours of police time.
• •Despite 500,000 facial scans, there were zero arrests, with only one alert generated, which was a false positive.
• •The gap between operational costs and effectiveness is highlighted by these specific figures.

PostHog released Jeeves, a project aimed at improving the performance of Jev-like decision models through reasoning processes.

• •Released by PostHog, Jeeves addresses the impact of reasoning on Jev-like decision models and potential improvements.
• •Titled "Jeeves – Reasoning improves Jev-like decision models," the project focuses on optimizing decision models.
• •Specific technical details and benchmark results are not included in the provided description.

The article provides visual guides on RNNs, CNNs, Transformers, and calibration, along with hands-on experiences regarding accuracy and efficiency.

• •It provides visual guides for RNNs, CNNs, Transformers, and calibration.
• •It includes hands-on experiments concerning accuracy and efficiency.
• •It explains the evolution of language models in text classification.

A locally runnable MoE model for cyber OSINT has been released, featuring supervised fine-tuning specialized for threat intelligence and investigation.

• •The model uses a MoE architecture with 26B total parameters, 4B active parameters, and a 262K context length, trained on 6,500 OSINT/CTI instruction data points.
• •Supervised Fine-Tuning was applied for cyber threat intelligence and investigation, supporting threat actor attribution, IoC pivoting, and geolocation.
• •It supports Admiralty Source Grading for source reliability assessment and can run in local environments.

An article examines the legality and privacy risks of modern cars collecting vast amounts of data, including location and driving behavior.

• •Modern cars collect detailed data on location, acceleration, braking, and steering intensity.
• •Similar to smartphones, cars are structured to record massive amounts of personal data.
• •The legal validity and privacy implications of this data collection are currently under discussion.

Raven, a multi-agent ecosystem designed for reliability, persistence, and self-evolution based on Recursive Self-Improvement, has been released.

• •Raven is designed as a self-evolving multi-agent ecosystem with reliability and persistence, aiming for collaboration across all domains.
• •Positioned as a "harness of harnesses," the project adopts an architecture with Recursive Self-Improvement in mind.
• •It was released under the repository name EverMind-AI/Raven and introduced to the community via Show HN.