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Panel: research workspace with agent-built panes

Panel is a local research workspace combining chat, files, PDFs, Markdown and Jupyter panes where an agent can read/write files and create custom viewers or apps. It requires Python 3.12+ and Claude Code for full features, runs at http://localhost:4173, and stores data in ~/Panel/panel.db and ~/Panel/workspaces. This is an early tester build with rough edges.

Hacker News · · Details

NeurIPS 2026 venue allocation concerns

NeurIPS 2026 will have multiple venues; Sydney passes sold out in minutes. Authors were asked to pick preferred locations but are not guaranteed to present there, raising concerns about a single 'main' venue concentrating resources and unfairly distributing presentations.

r/MachineLearning · · Details

Ordewell: ordered plans of coding-agent tasks

Ordewell turns one goal into an editable, typed plan of tasks where each task has its own runner, model, and mode and only completes when a unique completion marker appears. Plans can be rewritten before tokens are spent, preserve completed work, and support multiple runners (built-in Claude Code, Codex, OpenCode). Requires Node.js ≥20 and the TUI needs tmux.

Hacker News · · Details

AI underwriting firm raises $40M Series A

An artificial intelligence underwriting company raised $40M in a Series A round; the company name and investor details were not disclosed. The raise signals continued investor interest in applying AI to insurance underwriting.

FinSMEs · · Details

SHADOW-50M: 44M param 19.8MB offline LLM

The author trained SHADOW-50M from scratch (44M parameters on 45B tokens). The model is 19.8 MB, runs ≈1,900 tok/s on laptop CPU, uses ternary weights and a large token fingerprint vocabulary, operates fully offline, and includes built‑in calculator and fast disk indexing; released as a proof of concept.

r/MachineLearning · · Details

Anthropic confirms Claude used in weapons and surveillance

Anthropic confirmed that Claude was used to develop military software and support intelligence operations, including radar analysis, ranking air-defense targets, and aiding electronic warfare. In one Taiwan scenario it modeled engagement envelopes for Patriot- and THAAD-class systems and evaluated targets.

The Defense Post · · Details

Jensen Huang projects $3–4T AI market by 2030

NVIDIA CEO Jensen Huang said the semiconductor industry will keep growing and projected the AI market could be worth $3–4 trillion by 2030, arguing demand for smarter models will drive expansion; the piece also notes Nvidia’s recent revenue growth acceleration.

The Motley Fool · · Details

Report: China narrows AI gap with U.S.

The report says China has been rapidly closing its AI lead over the U.S. in model capabilities and research despite U.S. export restrictions on advanced chips and equipment. Experts describe the U.S.–China model gap as narrowing, noting Chinese efforts such as Moonshot AI’s Kimi K3.

ABC News - Breaking News, Latest News and Videos · · Details

Open Chinese models narrow gap with frontier models

A Mozilla report finds that the performance gap between Chinese open-weight models and US frontier models has narrowed to roughly 4.4 months. It highlights Moonshot AI's Kimi K3 scoring three points behind Anthropic's Fable 5 on a composite index while costing about 30% as much, suggesting open models are preferable for most routine workloads.

Ars Technica · · Details

Salesforce debuts Koa reasoning model on Nvidia Nemotron

At Dreamforce Salesforce unveiled Koa, its first reasoning model built on Nvidia's open-weight Nemotron, optimized for sales, marketing and customer-support tasks. Koa offers enterprises an open-weight alternative that was post-trained without ingesting customer data, integrates into Agentforce, and aims to reduce token costs while meeting data-security requirements.

TechCrunch · · Details

Critique: 1Password's AI Patching Benchmark

A critique of 1Password's August 6, 2026 report says its 26% "clean fix" rate is misleading: the sample focused on difficult bugs, 22% of trials instructed agents to apply wrong fixes, 36% forbade building or testing, and models used different reasoning settings. The authors also released agent skills for post-patch validation and review walkthroughs.

Hacker News · · Details