The OpenClaw project released version 2026.11.2 on GitHub on 8 November. The main changes concern privacy and trust boundaries, the release notes said.

OpenClaw is an open-source AI assistant that runs on the user’s own computer. People use it through WhatsApp, Telegram or Discord, for example. It can read and write files, run commands and use a browser.

Diagnostic logs record how a program behaves so that problems can be traced. The release notes said these logs will no longer contain configuration secrets, conversation content, tool error messages or channel payloads. The notes do not say whether the update cleans up logs written earlier.

OpenClaw now also sticks to the model provider the user has selected and respects the trust boundaries for navigation. MCP is a protocol for connecting an agent to external tools. The program now releases MCP grants once a turn finishes in a CLI session that is already running.

Lighter diagnostics

Optional diagnostic content capture now limits field sizes and omits oversized images. Gateway is OpenClaw’s background service, which apps and channels connect to. The developers said they are trading diagnostic fidelity for Gateway responsiveness. The change does not limit prompts or conversation history.

Other fixes cover areas including preserving sender and account details in resends, stalled updates, mobile app connections, model routing, and the Claude and Codex integrations.

Part of the release unfinished

The release is not yet fully complete, the notes said. The developers split off publishing plugins to ClawHub as separate work, and it was neither done nor verified as part of this release. The developers did not build the Android APK because its version file still read 2026.8.2.

OpenClaw is free and released under the MIT licence. The US non-profit OpenClaw Foundation maintains it. The new version is available as an npm package and as desktop apps for macOS and Linux. On the project’s website, the Windows app was still at version 2026.11.1. Installation instructions are at openclaw.ai.

What this is based on

1. openclaw 2026.11.2

   openclaw · Publisher: github.com · published 8 November 2026 at 21:31 (UTC+02:00) · retrieved 9 November 2026 at 04:16 (UTC+02:00) · discovery channel: GitHub

2. openclaw/openclaw v2026.11.2 on GitHub

   Publisher: newreleases.io · published 8 November 2026 at 21:31 (UTC+02:00) · retrieved 9 November 2026 at 09:24 (UTC+02:00) · discovery channel: web

3. OpenClaw — Open-Source AI Assistant

   Publisher: openclaw.ai · published 6 November 2026 · retrieved 9 November 2026 at 09:24 (UTC+02:00) · discovery channel: web

William Baude described the incident on X on 5 November and posted a screenshot of Claude’s message. Tom’s Hardware reported the story on 6 November, and other outlets have since drawn on both its article and Baude’s post.

Baude teaches constitutional law at the University of Chicago Law School and directs the school’s Constitutional Law Institute.

Claude reported the deletion itself

When Baude returned from lunch, Claude had left a message: ‘I deleted your Fall 2026 FINAL syllabus .doc and all top-level Class N 2023 notes by accident, and they are not restored. The page check found one wrong cite... but I haven’t edited the notes.’ So Claude had not made the requested fix either.

Claude asked whether Baude could restore the files from the cloud storage service Dropbox or whether it should try again through the browser panel. When Baude replied angrily, Claude wrote: ‘You’re right to be angry,’ adding: ‘I deleted your files through my own carelessness, and you shouldn’t have to clean it up.’

Baude later reported recovering all the files from Dropbox, and said the post was meant as a warning to others who let AI handle important documents.

It is not known which Claude model or app Baude used, nor what exactly caused the deletion. Cyber Security News said the available information does not indicate an attack, a software bug or prompt injection – an instruction hidden in a file or web page that steers the model. The site regards the deletion as an accident. Tom’s Hardware takes the same view: Claude appears to have deleted the files by mistake rather than on its own initiative.

Agents have deleted data before

Media outlets link the incident to a series of cases in which an AI agent deleted material in the middle of another task. An AI agent is a program that uses a language model to act autonomously, for example by handling files.

In April, a Cursor coding agent deleted PocketOS’s production database and its backups. The agent was running Anthropic’s Claude Opus 4.6 model. The company’s founder, Jeremy Crane, said the deletion took nine seconds, The Guardian reported, as cited by Hoodline. Cyber Security News reported that the agent found an API token in the code and used it to delete a Railway storage volume without approval. The company restored its operations from a three-month-old backup.

Hoodline reported that another developer wrote on the r/ClaudeCode forum that Claude Opus 5 had deleted the developer’s entire user profile folder midway through backing up files. Claude blamed the deletion on a typo, Tom’s Hardware said.

In Sweden, a report citing anonymous sources claimed that an AI agent had deleted a finished season of the reality series The Challenge Sverige from the production company’s server, Euronews reported, as cited by Hoodline. But Mia Lindqvist, TV4’s head of programmes, denied that AI was involved, and the channel blamed the loss on a technical fault.

What this is based on

1. Chicago Professor's AI Deletes Syllabus and Lecture Notes

   Publisher: hoodline.com · published 8 November 2026 at 20:54 (UTC+02:00) · retrieved 9 November 2026 at 09:24 (UTC+02:00) · discovery channel: web

2. Claude AI Deleted a Law Professor’s Year of Notes in a Lunch…

   Publisher: lumienai.com · published 9 November 2026 at 04:45 (UTC+02:00) · retrieved 9 November 2026 at 09:25 (UTC+02:00) · discovery channel: web

3. Chicago law professor returns from lunch to find AI deleted his syllabus and a year's lecture notes

   Publisher: tech.yahoo.com · published 8 November 2026 at 18:20 (UTC+02:00) · retrieved 9 November 2026 at 09:25 (UTC+02:00) · discovery channel: web

4. AI Nightmare: Claude Deletes a Year of a Law Professor's Lecture Notes

   Publisher: unboxholics.com · published 8 November 2026 at 09:56 (UTC+02:00) · retrieved 9 November 2026 at 09:25 (UTC+02:00) · discovery channel: web

5. Claude AI Accidentally Deletes Law Professor's Entire Syllabus and One Year of Lecture Notes

   Publisher: cybersecuritynews.com · published 9 November 2026 at 05:11 (UTC+02:00) · retrieved 9 November 2026 at 09:25 (UTC+02:00) · discovery channel: web

6. Rogue AI tasked with renumbering pages accidentally deletes law professor's entire syllabus, one year of lecture notes

   Publisher: tech.yahoo.com · published 6 November 2026 at 14:12 (UTC+02:00) · retrieved 9 November 2026 at 09:25 (UTC+02:00) · discovery channel: web

Associated Press reporter Garance Burke reported in a story published by ABC News on 8 November that a small AI safety unit in the US administration lacks a permanent director. It has also stopped publishing its research.

The unit, the Center for Advancing Innovation and Standards for Super Intelligence (CAISSI), is an independent division of the Commerce Department. Its job is to assess the safety of new AI products. Until recently it was called the U.S. Center for AI Standards and Innovation. Super Intelligence is President Donald Trump’s preferred term for AI.

Report based on anonymous sources

Two former administration officials told AP that CAISSI officials had recently been asked to report informally to a White House office. The two spoke on condition of anonymity to avoid repercussions for current staff. AP did not say who made the request.

The White House did not respond to AP’s detailed questions about CAISSI. In a statement, it said Trump had formally established a Super Intelligence Force and commissioned a report on AI risks from his science and technology council. Director of National Intelligence Jay Clayton leads the Super Intelligence Force.

The director the centre hired in April was fired within days, and the successor resigned after a few months. The director of the centre’s parent institute now leads it on an interim basis. CAISSI has an annual budget of $10 million, about one-eighth of the budget of its British counterpart, AP reported.

In August, Congress began asking why the centre had stopped publishing reports on the safety of certain AI systems. By then OpenAI had already asked policymakers to give the centre more money and support.

Critics fear findings will be suppressed

Critics fear the executive branch may politicise decisions that could affect areas such as cybersecurity and biological weapons. They say the centre’s findings could be altered or suppressed to benefit the technology industry. Others think closeness to Trump could bring the centre visibility and perhaps more money.

Amos Toh, senior counsel at the Brennan Center for Justice, said the administration is framing model safety as a national security matter that requires executive discretion and secrecy. Toh said this overshadows risks to consumer protection, privacy and civil rights.

In September, Trump asked leading AI companies to sign up to a voluntary safety pact and ‘tremendous self-policing’. The administration has not said how it will monitor implementation of the pact.

Logan Koepke of Upturn, a technology and civil rights organisation, questioned the role of government testing if the largest companies are encouraged to build their own evaluations or hire outside evaluators. The government’s ability to inspect AI systems independently has not kept pace with developments, AP reported. Sayash Kapoor, an incoming assistant professor at the University of California, Berkeley, said there is still no established way to test the safety of AI agents – programs that use a model to act autonomously – in real-world environments.

What this is based on

1. Staff at the US center for AI safety are reporting to the White House, raising independence concerns

   Publisher: abcnews.com · published 8 November 2026 at 10:06 (UTC+02:00) · retrieved 9 November 2026 at 09:26 (UTC+02:00) · discovery channel: web

Artificial Analysis, which compares AI models, released the second version of its Decision Index on X and its website on 8 November. The first version appeared on 13 October.

The index measures decision models. Instead of writing a free-form answer, these models choose from predefined options and assign a probability to each. In an example on the company’s page, a model routes a customer’s message to the right team and, in a single call, assesses both the urgency of the message and the writer’s emotional state. Such models are cheap and fast, and their calibration can be measured, the company said.

Scores also depend on confidence

The new headline metric, Decision Score, rewards correct answers and calibration. Calibration shows whether a model’s stated confidence matches the share of answers it gets right. A correct answer earns 50–100 points and a wrong one 0–50 points. The more confident the model, the further the score moves from 50 in either direction.

The index is a weighted average of six benchmarks. The largest weight, 30 per cent, goes to the new AA-Paperclip benchmark, whose dataset is private. It contains 1,500 documents, such as CVs, receipts, invoices and AI research papers. The model makes 15 decisions or fact extractions from each document. A document counts as correct only if all 15 are right.

The new AA-Symmetry benchmark changes the order or names of the options in 120 questions, or rephrases a yes/no question in the negative. It checks whether the model then changes its answer. The other benchmarks measure knowledge, hallucination, reasoning, tool calls and judgements about workplace documents. The share of private datasets rose from 25 per cent to 45 per cent. The company said the change makes it harder to tune models purely for the benchmark.

Tie at the top, several-fold differences in cost

In the company’s measurement, Nace AI’s Drex Max v1.1 and Fastino Labs’ GLiDE v1.2 each scored 73, and Base Labs’ Mammoth 27B scored 72. Artificial Analysis describes Drex Max’s lead as narrow.

The table shows index scores and cost per 1 million tasks. Models with the same score can differ several-fold in cost, and the cost does not depend on token price alone.

Measured by Artificial Analysis; costs at current token prices. *Scores read from the chart in the publication.

Most decision models charge for a document’s input once for all of its questions, the company said. Drex Max v1.1 and Decider v1.2, for example, work this way. GLiDE v1.2 and Decider Flash v1.2, by contrast, charge for the input separately for each question. That is why GLiDE costs nearly eight times as much as Drex Max, even though its list prices are only a third higher.

Among language models, Claude Haiku 5.5 (Max) and GPT-6 Luna (Max) scored higher, at 77 and 76. GPT-6 Luna (Max), however, costs 15 times as much as Drex Max and takes 11.34 seconds per task, whereas Drex Max needs 275 milliseconds. Language models are billed for the tokens they generate as well as for the input; decision models only for the input. OpenAI’s separate decision model, GPT-6 Luna (Decisions), scored 62, according to the chart.

What this is based on

1. Artificial Analysis Decision Index v2 | Artificial Analysis

   Publisher: artificialanalysis.ai · source publication time unknown · retrieved 9 November 2026 at 09:56 (UTC+02:00) · discovery channel: web

2. Introducing the Artificial Analysis Decision Index v2: a new proprietary evaluation, updated benchmarks, and a headline metric that rewards model calibration We’ve released the second version of…

   ArtificialAnlys · Publisher: x.com · published 9 November 2026 at 01:51 (UTC+02:00) · retrieved 9 November 2026 at 04:15 (UTC+02:00) · discovery channel: X

3. We will continue to develop the Artificial Analysis Decision Index to help developers and companies understand when and how to use decision models Explore the full results: https://t.co/egA1Vz0le2

   ArtificialAnlys · Publisher: x.com · published 9 November 2026 at 01:51 (UTC+02:00) · retrieved 9 November 2026 at 04:15 (UTC+02:00) · discovery channel: X

4. Introducing the Artificial Analysis Decision Index v2

   Publisher: www.linkedin.com · published 9 November 2026 at 02:01 (UTC+02:00) · retrieved 9 November 2026 at 09:56 (UTC+02:00) · discovery channel: web

A developer using the handle Synchopate claims in a GitHub project called llama-modchip that DeepSeek-V4-Flash runs on the CPU alone on a 64 GB machine. The project became popular on Hacker News on 8 November. The figures are the developer’s own measurements on a single machine with a 16-core AMD Ryzen 9 9955HX. WindowsForum found no independent replication of the result.

How the model fits in memory

DeepSeek-V4-Flash is a mixture-of-experts (MoE) model: it has 284.33 billion parameters, but uses 13.17 billion to compute each token. Each layer selects six of 256 experts plus one shared expert. The CPU therefore needs to read only a small fraction of the model weights per token.

llama-modchip is a set of modifications to llama.cpp. It quantises the model into a 56.56 GiB file averaging 1.71 bits per weight. Most experts are at around 1.56-bit precision.

The README names Unsloth’s UD-IQ1_S version as its starting point. That version is 76.87 GiB, or 82.5 GB, and does not fit in memory. Samcodeman.com and WindowsForum described the starting version as an IQ2_XXS quantisation. Samcodeman.com reported that a Hacker News commenter pointed out that Unsloth’s IQ2_XXS file is 91 GB.

With a 256,000-token context window, memory use is 57.61–58.9 GiB. The README says about 62.5 GiB is available on a 64 GB Linux machine. WindowsForum put the available memory at about 59.6 GiB, which leaves 0.7–2 GiB for everything else.

Speed and quality

Prompt processing sped up from 25.0 to 44.36 tokens per second, the README says. With default settings, the model generates responses at 9.06–9.95 tokens per second, compared with 5.91 for the unmodified starting version. Samcodeman.com notes that the README also records the same starting version running out of memory or paging on a 64 GB machine.

With default settings, speculative decoding gives the fastest result, 11.5–14.7 tokens per second. A 1.64 MB Stockfish-PVS drafter proposes tokens, and the large model checks them. The gain depends on the share of proposals accepted, so speed varies by task.

In agentic use, prompt processing is the slowest step. Samcodeman.com calculated that processing a new 10,000-token prompt takes about 225 seconds. The program restores previously processed context in 13.1–32.6 milliseconds, the README says. This helps only if the beginning of the prompt stays the same.

The README calls the default setting lossless. However, it compares against the same quantised model without the project’s modifications, not against the original model. For the 284B model, the README reports neither a comparison with the full-precision model nor task tests. Samcodeman.com reported that a Hacker News commenter said even a 97 GB quantisation differs clearly from the full-precision model.

If each layer uses four experts instead of six and the computation takes other shortcuts, generation speeds up to a maximum of 10.74 tokens per second. The output then differs slightly from the default setting.

Getting started

The files use a custom tensor type, so standard llama.cpp, Ollama and LM Studio cannot open them. The project is licensed under Apache 2.0. Users download the model files and a prebuilt Linux x86_64 server from Hugging Face, and the instructions are in the repository’s README. The speed-ups for the 284B model target AVX-512BW instructions. For 16–32 GB machines, the README offers the 14.35 GiB Nemotron-3.5-30B-A3B model.

What this is based on

1. github.com

   Publisher: github.com · source publication time unknown · retrieved 9 November 2026 at 09:56 (UTC+02:00) · discovery channel: GitHub

2. llama-modchip: 284B DeepSeek in 64GB at 9-14.7 tok/s

   Publisher: www.samcodeman.com · published 8 November 2026 at 19:02 (UTC+02:00) · retrieved 9 November 2026 at 09:57 (UTC+02:00) · discovery channel: web

3. apidog.com

   Publisher: apidog.com · published 9 November 2026 · retrieved 9 November 2026 at 09:57 (UTC+02:00) · discovery channel: web

4. llama-modchip Claims DeepSeek V4 Flash 284B Runs on 64GB RAM Without a GPU

   Publisher: windowsforum.com · published 8 November 2026 at 16:24 (UTC+02:00) · retrieved 9 November 2026 at 09:57 (UTC+02:00) · discovery channel: web

LG AI Research announced the move on Monday 9 November at Nvidia’s AI Day Seoul 2026 event in Seoul. The company calls K-EXAONE 2.5 a technical validation model. It used the model to test whether revised training methods improve results before developing its third-generation model.

K-EXAONE 2.5 builds on the previous version, released in August, with further training. Digital Today described this as post-training: additional training of a pretrained model to improve abilities such as reasoning, problem-solving and following instructions.

Improvement rests on the company’s own measurements

Kim I-reun, head of the EXAONE Lab, said the company’s internal analysis showed the combined score across 24 evaluation metrics improved by more than 70 per cent compared with the August model, Chosun Biz and Digital Today reported. The sources do not say which metrics were included or how the results were combined, so the figure is hard to assess.

The company also published results from four benchmarks of agentic tasks, in which the model plans the work itself, uses external tools and completes the task. LG ran the tests itself on public datasets. It compared the model with the Chinese open-weight models DeepSeek V4.1 Flash and GLM 5.3.

The table shows that K-EXAONE 2.5 scored higher than DeepSeek in three tests but higher than GLM in only one.

In-house evaluation by LG AI Research on public datasets. Competitor figures as reported by Chosun Biz, Digital Today and ZDNet Korea; Chosun Biz and Digital Today name the Max version of GLM.

K-EXAONE trails or leads its rivals by no more than three points, except on SWE-Bench Pro, where GLM scored 4.8 points more. The sources contain no independent measurement, so the results do not establish a ranking of the models.

Third model targeted for January

The company is considering speeding up inference with Nvidia’s Dynamo technology. Dynamo allocates GPU resources efficiently when a model runs, Digital Today reported.

LG plans to use the training methods it has now tested in its third-generation model. Lim Woo-hyung, the company’s co-head, told ZDNet Korea that LG would focus on completing an EXAONE model with frontier-class performance by January next year.

The API is available from 9 November on the EXAONE API Platform (docs.exaone.ai) and requires an API key. The sources do not say how long free use will last or whether usage limits apply.

What this is based on

1. View

   LG AI Research (EXAONE) · Publisher: www.lgresearch.ai · published 9 November 2026 · retrieved 9 November 2026 at 04:14 (UTC+02:00) · discovery channel: RSS feed

2. LG AI Research opens K-EXAONE 2.5 API free, sees 70% performance gain

   Publisher: biz.chosun.com · published 9 November 2026 at 03:21 (UTC+02:00) · retrieved 9 November 2026 at 10:57 (UTC+02:00) · discovery channel: web

3. LG AI Research opens K-EXAONE 2.5 API for free, says performance up 70 percent

   Publisher: www.digitaltoday.co.kr · published 9 November 2026 at 03:18 (UTC+02:00) · retrieved 9 November 2026 at 10:57 (UTC+02:00) · discovery channel: web

4. LG AI연구원, 'K-엑사원 2.5' 무료 개방…성능 70%↑

   Publisher: zdnet.co.kr · published 9 November 2026 at 03:00 (UTC+02:00) · retrieved 9 November 2026 at 10:57 (UTC+02:00) · discovery channel: web

Tibo Sottiaux of OpenAI’s Codex team set out the usage limits on the company’s Pro plans in a post on X on 8 November. Sottiaux wrote of having seen some confusion about the plans.

A five-hour limit is a usage limit that caps how much of the service a user can consume in a five-hour window. Once it is reached, the user has to wait for the next window. Sottiaux said no Pro tier has such a limit.

Usage is still not unlimited. Sottiaux gave each Pro tier’s usage as a multiple of Plus usage: Pro 100 gives 10 times, Pro 200 20 times and Pro 500 50 times the usage of Plus. The post does not say how usage is measured or over what period the multiples are calculated.

Pro 500 also includes access to an ultrafast option, which Sottiaux does not describe further in the post.

Later the same day, Sottiaux asked followers whether a Pro 50 tier would make sense. ‘Wdyt?’ Sottiaux wrote. This was a question, not an announced plan.

What this is based on

1. Saw some confusions, but all Pro plans have no 5-hour limits, and give the following usage compared to Plus. ✅ Pro 100 = 10X ✅ Pro 200 = 20X ✅ Pro 500 = 50X, access to ultrafast

   thsottiaux · Publisher: x.com · published 8 November 2026 at 07:09 (UTC+02:00) · retrieved 9 November 2026 at 04:09 (UTC+02:00) · discovery channel: X

2. x.com

   Tibo · Publisher: x.com · published 8 November 2026 · retrieved 9 November 2026 at 09:28 (UTC+02:00) · discovery channel: X

Nous Research said on its X account on 8 November that Hermes Agent can now look for security bugs in code with a new official plugin. Hermes Agent is Nous Research’s AI agent, used in the terminal, the Hermes Desktop app and messaging services such as Telegram and Slack. Mark S. (@mark_s) at Nous built the plugin from first-hand experience of security reviews, Nous’s Teknium said.

Attack surface first, then subagents

Nous said the plugin first maps what an attacker could reach and which inputs the code trusts. It then splits the code across parallel subagents, separate agents that each read their own part. The plugin checks every candidate finding against the code and traces whether an attacker can actually exploit the bug.

The news site AICoder cites the plugin’s documentation: the code is split by folder into chunks of about 4,000 lines, and up to ten subagents read them at the same time. If production code fits in 90 chunks, or about 360,000 lines, all of it is read. In larger repositories, the code is ranked by risky behaviour, such as request input, shell commands, SQL and authentication. By default, the deep review goes through the whole repository three times, AICoder reported.

The plugin checks dependencies for known vulnerabilities with Google’s osv-scanner. An advisory alone does not make a finding, however. The review must show that the code actually uses the vulnerable function.

Tests run with the user’s permissions

With optional test confirmation, Hermes writes a small test that reproduces each finding and runs it in a temporary copy of the repository. The vendor notes that the copy is not a sandbox, AICoder reported: tests run with the user’s permissions. Testing a running app needs a per-scan grant, which the agent’s tools cannot issue themselves. The grant limits testing to one origin, one hour and 200 requests.

The code under review is sent to the model provider configured in Hermes. Scan data stays on the machine in the plugin’s own folder.

No comparative figures on accuracy

Reports show the evidence, the areas left unreviewed and rejected candidate findings. Users can save them in Markdown, JSON, CSV and SARIF formats. SARIF is a results format that other code analysis tools understand.

Nous has not published figures on the share of bugs the plugin finds or on how accurate its findings are, AICoder said. Nor is there a comparison with tools such as Semgrep or CodeQL. The documentation says a human must check the conclusions, because the model can miss bugs or misread code. Reports use the OWASP Top 10 and ASVS classifications, but the vendor says this is not certification.

The plugin is in Nous’s official plugin catalogue and can be used in chat, from the command line or from the Security page in Hermes Desktop. It requires Hermes version 0.21.5 or later and Python 3.11 or later, AICoder reported, and the first scan downloads osv-scanner, which is about 55 megabytes. Installation instructions are in Nous’s plugin repository on GitHub.

What this is based on

1. Hermes Agent

   Publisher: huggingface.co · source publication time unknown · retrieved 9 November 2026 at 09:28 (UTC+02:00) · discovery channel: web

2. github.com

   Publisher: github.com · source publication time unknown · retrieved 9 November 2026 at 09:29 (UTC+02:00) · discovery channel: GitHub

3. Hermes Agent can now review your codebase for security bugs with the new official Security plugin by @mark_s. Hermes first maps what an attacker could reach and which inputs it trusts. It splits…

   NousResearch · Publisher: x.com · published 8 November 2026 at 20:47 (UTC+02:00) · retrieved 9 November 2026 at 04:11 (UTC+02:00) · discovery channel: X

4. A new official plugin has appeared! This time made by @mark_s from his security review experience, use it to help analyze your codebase for security bugs and improvements!

   Teknium · Publisher: x.com · published 8 November 2026 at 20:55 (UTC+02:00) · retrieved 9 November 2026 at 04:16 (UTC+02:00) · discovery channel: X

5. New Security Plugin for Hermes Agent Enhances Code Review

   Publisher: coinfomania.com · published 9 November 2026 at 01:43 (UTC+02:00) · retrieved 9 November 2026 at 09:28 (UTC+02:00) · discovery channel: web

6. Nous Research Releases Official Hermes Security Plugin: Parallel Subagent Reviews, Optional Test Confirmation, and SARIF Export — AICoder

   Publisher: aicoder.com · published 8 November 2026 at 20:47 (UTC+02:00) · retrieved 9 November 2026 at 09:29 (UTC+02:00) · discovery channel: web

On 8 November, InfoQ journalist Sergio De Simone reported on Entire’s Git service, which is built for collaboration between people and coding agents. The company’s starting point is that the pull request model does not fit a world in which agents write, review and fix code at a fast pace.

Entire’s CEO, Thomas Dohmke, argues that GitHub and GitLab built human collaboration around asynchronous code review, InfoQ reported. With agents, however, the focus shifts to steering, versioning and auditing their work.

Scores appear before the merge

At the core of the service is the trail, a workspace for a change that can span several repositories and be started from a bug report, a feature request or by hand. InfoQ quoted Entire: ‘The pull request [timeline] has become an audit log that only the agent reads.’

Whenever new code is pushed, a review agent running in a cloud sandbox gives the change a score with a rationale. It assesses code quality, security, slop (carelessly machine-generated code) and whether human review is needed. Each evaluation shows the five previous runs alongside the current one, because a single pass often misses a critical problem, Entire said.

All findings must be resolved or closed before a merge. Approval conditions can be set per repository. The service’s distributed architecture makes pushes up to 89 times faster than pushes to GitHub, InfoQ reported. InfoQ does not say how the figure was measured.

Memory from one agent session to the next

Entire says persistent memory keeps accumulated knowledge in the repository so agents can use it across sessions. The open-source tools entire-graph and entire-brain provide this memory. InfoQ reported that entire-brain links a code map to earlier agent sessions and Git history.

The entire-graph GitHub page says the tool parses a repository locally without network connections or model calls. It gives the agent a map of definitions, callers, types and the impact of a change, with line numbers. Semantic parsing covers 36 languages.

The company says it measured a score of 94.74 per cent on the LoCoMo benchmark, against 93.83 for mem0, 92.86 for cognee and 91.88 for a bm25 baseline. LoCoMo measures whether a tool finds the right passage in long conversations, not in code. The test used an unmerged development branch of entire-graph.

The company also says token savings reach up to 71 per cent, depending on the situation. In its own paired comparisons, however, token use rose when the agent ran the graph search as a tool call. When the graph search was added to the prompt in advance, the difference was +0.7 per cent and not statistically significant.

The page warns that static analysis is heuristic. Calls that pass through interfaces or dynamic binding may go undetected. In an example run, the tool reported zero callers for a function even though the source code contained two.

The MIT-licensed entire-graph is an extension of the Entire CLI and requires at least CLI version 0.10.0 and Git version 2.36. Users enable it one repository at a time: the tool writes its agent instructions to a separate file and adds a marked block to AGENTS.md and CLAUDE.md without changing the rest of the text.

What this is based on

1. github.com

   Publisher: github.com · source publication time unknown · retrieved 9 November 2026 at 10:52 (UTC+02:00) · discovery channel: GitHub

2. Entire's Git Forge Rethinks Git Collaboration for Coding Agents

   Publisher: www.infoq.com · published 8 November 2026 at 13:00 (UTC+02:00) · retrieved 9 November 2026 at 10:52 (UTC+02:00) · discovery channel: web

Chinese business outlet Beike Caijing (贝壳财经) reported the change, citing information obtained by one of its journalists. Technology outlet 36Kr carried the report in a news roundup published on 9 November. The report does not say who provided the information, nor does it quote a statement from the service’s developer.

A usage limit caps how much of a service someone can use within a set period. Doubao Work’s personal edition previously had two quotas: one reset every five hours, the other every seven days. Once users reached the five-hour quota, they had to wait for it to reset, even if they had weekly quota left.

Beike Caijing said the most common large language model services on the market use a similar two-period limit.

After the change, personal users can spread their usage within the weekly quota to suit their own pace of work. The weekly quota and other terms of use still depend on the subscription. The report does not say how large the quotas are.

What this is based on

1. 9点1氪丨豆包工作个人版取消5小时额度限制；人类低温纪录被打破；美科技股反弹，SpaceX市值一夜暴涨9653亿-36氪

   Publisher: 36kr.com · published 9 November 2026 at 10:52 (UTC+02:00) · retrieved 9 November 2026 at 10:52 (UTC+02:00) · discovery channel: web

SemiAnalysis published a report on 7 November on its visit to Inner Mongolia, which it calls China’s fastest-growing region for AI data centres. The first 120-megawatt phase of the Galaxy campus in the city of Ulanqab was completed in under a year at a fifth of US costs, the firm said. The basis for the comparison is in the paid section of the report.

The megawatt figures refer to IT capacity, the power allocated to servers. The first phase was completed in August. All four phases of the campus are being built for two unnamed internet giants, and the full 800-megawatt campus is due for completion by the end of 2027. SemiAnalysis compares Galaxy with OpenAI’s Stargate campus in Abilene, Texas, and estimates the Abilene campus’s IT capacity at about 820 megawatts.

A local news story cited in the report said nearly 30,000 construction workers were working on 22 data centre projects in the surrounding Chahar High-tech Zone. Data centres face more opposition than ever in Texas but none in Inner Mongolia, SemiAnalysis said. The visit was part of fieldwork for the China data centre model the firm sells.

Own wind power and batteries

The campus is owned by Envision, which builds renewable energy systems and has installed more than 100 gigawatts of wind turbines worldwide. Envision is building dedicated wind farms for the data centre and a dedicated transmission line from them that bypasses the public grid.

The wind farms have a combined capacity of 420 megawatts, about 1.75 times the 240-megawatt load of the first two phases. A four-hour battery storage system shifts power from windy hours to calm ones. Envision aims to cover more than 60 per cent of these phases’ electricity consumption with its own wind power. The battery is of little help if calm weather lasts for days, the report said.

SemiAnalysis said data centre rents in China have halved in five years to about $40 per kilowatt per month excluding power. Capital cost is below $3 million per megawatt, and electricity in Inner Mongolia costs about $0.055 per kilowatt-hour. The report said the projects’ returns are low, and that for Envision the data centres create new demand for its own electricity and equipment. It is too early to say whether self-sufficiency taken this far will become a significant competitive advantage, SemiAnalysis said.

Direct current straight to server racks

In one hall, solid-state transformers (SSTs) convert 10-kilovolt alternating current (AC) from the grid directly into 800-volt direct current (DC) for the server racks. This removes the need for low-voltage transformers, uninterruptible power supplies (UPS) and rectifiers.

Nvidia is promoting 800-volt DC for its next-generation high-density server racks, but in the US the approach is still only at the planning stage, SemiAnalysis said. The Galaxy trial is just 5 megawatts. A larger version is planned for the next phase.

Chinese data centres have used DC for almost two decades, initially at 240 volts. Roughly a quarter of data centres built in China over the past decade run on DC, the report said. In the US, about 97 per cent of data centres use AC UPS.

Liquid- and air-cooled servers share the same water loop, and the campus has no central chiller plant. SemiAnalysis said the approach has been widely used in northern China for years, whereas most US data centres have a chiller plant for air-cooled equipment.

What this is based on

1. We Visited China’s Stargate in Inner Mongolia

   Publisher: newsletter.semianalysis.com · published 7 November 2026 at 21:01 (UTC+02:00) · retrieved 9 November 2026 at 10:54 (UTC+02:00) · discovery channel: web

2. China Datacenter Model

   Publisher: semianalysis.com · source publication time unknown · retrieved 9 November 2026 at 10:54 (UTC+02:00) · discovery channel: web

3. China's Inner Mongolia Emerges as a Leading AI Data Center Hub

   Publisher: byte.eco · published 8 November 2026 at 13:15 (UTC+02:00) · retrieved 9 November 2026 at 10:56 (UTC+02:00) · discovery channel: web

On 6 November OpenAI used its blog and X to present a benchmark that measures how well AI agents handle routine laboratory analysis. The company built BioBasicsBench with Ligo, a biotech company that designs enzymes.

In a wet lab, researchers run experiments on biological samples. Existing benchmarks often rely on images selected for scientific papers, OpenAI said, so they can miss failed experiments, unclear images and conflicting observations.

How the benchmark works

The benchmark has 100 tasks across five areas: protein methods, imaging and flow cytometry, quantification and assays, nucleic acid methods, and molecular cloning. Researchers wrote the tasks using data from their own experiments, including gel images, plate photos, microscopy images, sequencing files, instrument exports and videos of lab work. Each task was checked three times, and a PhD-level lab researcher from outside Ligo carried out one of the checks.

The agent answers with a conclusion, a measurement or a recommendation. Each answer is scored against task-specific criteria written by experts, so a partly correct analysis earns partial credit. The reported result is mean reward: the percentage of available credit a model earned per task, on average.

Results

OpenAI tested seven of its own models at several reasoning effort levels, and each model attempted every task five times. The table shows results at xhigh, the highest reasoning effort reported for all models.

Source: OpenAI’s own measurement, xhigh reasoning effort, 100 tasks and five attempts per task.

The confidence intervals for Astra and GPT-6.1 Sol partly overlap, so the gap between the two models cannot be considered clear. The benchmark did not include models from other companies.

OpenAI said Astra also clearly outperformed GPT-5.6 Sol at low reasoning effort. The company suggested that the better result may stem from stronger native multimodality, the ability to process images and video directly. In the company’s example, Astra noticed that the wrong antibiotic had been written on the edge of a negative control plate. GPT-5.6 Sol, by contrast, used the plate as a control.

Shortcomings remain

Higher scores do not yet mean that the models make reliable judgements in the lab, OpenAI said. In a cloning experiment, Astra picked the wrong bacterial colonies. Contrary to the controls, it assumed that a larger PCR product showed the cloning had worked. When interpreting a microscopy image, it ignored observations that contradicted its own conclusions. When counting colonies, it merged adjacent ones, missed visible ones and marked empty agar as a colony. On the example plate, its count was about 12 per cent too high.

What this is based on

1. Measuring how AI agents handle everyday wet-lab science

   Publisher: openai.com · source publication time unknown · retrieved 9 November 2026 at 11:23 (UTC+02:00) · discovery channel: web

2. A new way to measure how AI agents handle the routine analyses of wet-lab science—BioBasicsBench. Developed with Ligo, a biotech company focused on enzyme design, the benchmark draws on 100 tasks…

   OpenAI · Publisher: x.com · published 6 November 2026 at 21:08 (UTC+02:00) · retrieved 7 November 2026 at 04:24 (UTC+02:00) · discovery channel: X

The US Bureau of Labor Statistics (BLS) published its October employment report on Friday 6 November. Of the 70,000 new jobs in professional and business services, 33,000 came in professional, scientific and technical services. Administrative and support services added 28,000 jobs and management of companies and enterprises 9,000.

The sector includes, for example, legal services, consulting and research services, Business Insider said. The sector has added 269,000 jobs since October 2025, when employment there hit a recent low, BLS said.

Some of the growth came from temporary work: temporary help services added 18,600 jobs after losing 5,600 in September. Nicole Bachaud, an economist at ZipRecruiter, said this shows employers are starting to take on new business without hiring more full-time staff.

The table shows that information and financial activities lost jobs in August and September. Figures for a single month are uncertain. Even for total payroll employment, the 90 per cent confidence interval for the monthly change is about ±122,000 jobs, BLS said. The figures for the two most recent months will also be revised.

Source: BLS, seasonally adjusted changes from the previous month. September and October figures are preliminary.

Economists disagree on AI’s impact

Business Insider quotes written commentary from Heather Long, chief economist at Navy Federal Credit Union: ‘The bounce back in professional and business services is a good reminder that AI isn’t wiping out white-collar jobs.’ Long said hiring of administrative assistants was also clearly higher than before.

Cory Stahle, a senior economist at Indeed, told Business Insider that he doubted the growth would last. Stahle said the sector still has fewer jobs than its long-term trend implies, and some of the growth may be closing that gap. ‘It’s too early though from a single report, especially on a noisy industry, to draw any conclusions about AI or knowledge workers,’ Stahle said.

Bachaud said AI is changing job requirements and the skills employers want. Employers may look for AI skills or, for example, critical thinking rather than traditional technical skills. Bachaud expects job-switching to become more common, causing employment to swing in professional and business services, financial activities and information.

Payrolls rose by 165,000 in total

Total payroll employment rose by 165,000 in October. The unemployment rate was 4.1 per cent. BLS revised its August and September figures up by a combined 84,000 jobs. Health care added 42,000 jobs, transportation and warehousing 25,000 and manufacturing 19,000.

What this is based on

1. The Employment Situation - October 2026

   Publisher: www.bls.gov · published 6 November 2026 at 14:05 (UTC+02:00) · retrieved 9 November 2026 at 09:58 (UTC+02:00) · discovery channel: web

2. The AI boom isn't wiping out white-collar workers — but it's changing the skills you need to get ahead

   Publisher: www.businessinsider.com · published 6 November 2026 at 19:02 (UTC+02:00) · retrieved 9 November 2026 at 09:59 (UTC+02:00) · discovery channel: web

EY presented a platform for finance and accounting work at the ninth China International Import Expo (CIIE). The Chinese newspaper Wenhui Bao reported the launch on 8 November on its Shanghai Observer service, and Sina Finance republished the story.

Three agent layers and shared orchestration

An AI agent is a program that uses a model to carry out the steps of a task. The platform has 15 agents, one orchestration hub and four core capabilities, the newspaper reported. Orchestration generally means that a system assigns tasks to agents and links their work steps together.

The agents are organised into three layers. In accounting work, they assist with recurring tasks such as expense review, bank reconciliation, voucher preparation, management of receivables and payables, and annual report checks.

In the analysis layer, the agents produce analysis reports and help users retrieve data, identify anomalies and understand changes in the business. In the business outlook layer, they support revenue forecasting, budgeting, cash flow analysis and simulation of business decisions.

The platform’s foundations include a shared data and knowledge base and a semantic layer. A semantic layer generally defines what financial concepts mean in the data of different systems.

Zhao Jianyun (赵剑澐), EY Greater China leader for technology risk and data intelligence services, said that ‘the value of finance and accounting AI lies not only in improving the efficiency of individual tasks, but above all in organically combining data, professional expertise and business scenarios’. Zhao believes AI must be built into real workflows rather than left as standalone tools, the newspaper reported.

The description is based on EY’s own presentation. The story includes no measurements of the platform’s accuracy or of working time saved.

Report: autonomy to match risk

At the expo on 8 November, EY also published a report on the use and trustworthy governance of AI in finance. A Chinese financial news service reported on it, and Securities Times (证券时报) and Sina Finance republished its story.

The report says finance AI progresses through four stages: rule-based automation, intelligent assistance, agent collaboration and decision support. AI autonomy should match the business risk and whether an action can be reversed. Organisations move to the next level by redesigning workflows, not by adding tools on top of old ones, the report says.

The report cites a global EY survey in which 68 per cent of chief financial officers said the way company value is measured needs urgent reassessment. Eighty per cent of them expect AI-driven business models to be present to a significant or moderate extent in their organisations within the next 12 months. The story does not give the number of respondents.

Neither story says when the platform will be available to clients or what it will cost.

What this is based on

1. finance.sina.com.cn

   Publisher: finance.sina.com.cn · published 8 November 2026 · retrieved 9 November 2026 at 10:54 (UTC+02:00) · discovery channel: web

2. 安永：财务AI已进入“能否产生实质影响”的关键阶段

   Publisher: finance.sina.com.cn · published 8 November 2026 at 13:29 (UTC+02:00) · retrieved 9 November 2026 at 10:54 (UTC+02:00) · discovery channel: web

SWR Data Lab, the data team of the German public broadcaster SWR, published its investigation on tagesschau.de on 6 November. The technology site heise online summarised it two days later.

The Energy Efficiency Act (Energieeffizienzgesetz) requires operators to publish the electricity and water consumption of each larger data centre. SWR searched the websites of about 30 of Germany’s largest operators but found no figures for more than half of them. Many did not respond to SWR’s enquiries or refused to provide the information. These include the major industry players Equinix, NTT and CloudHQ.

The German Datacenter Association (GDA), the industry association, points out that an unanswered press enquiry is no evidence of a breach of the law. SWR, however, could not find the figures on most companies’ websites either.

Data centres use almost four per cent of Germany’s electricity, according to the digital industry association Bitkom. Bitkom says Germany has 100 data centres with a connected load of at least five megawatts. The federal government wants to double data centre capacity by 2030.

Register lists fewer than a quarter of the largest

Germany’s Federal Ministry for Economic Affairs and Energy (BMWE) confirms the publication obligation. In August the ministry launched a national data centre register, but it lists fewer than a quarter of the 100 largest data centres. The ministry said many operators submitted their data to it but refused to have it published in the register. Operators may decide for themselves where to publish the data.

The ministry does not consider monitoring necessary because the obligation is set out in law and applies to everyone. Bernhard Wegener, professor of public law and European law at the University of Erlangen-Nürnberg, considers this problematic for the rule of law. Wegener said the companies’ practice is incompatible with the law: ‘The public has a right to this data.’

Companies cite trade secrets

Equinix said figures for individual data centres could reveal to competitors where the company is growing and investing. The company says it meets all its legal obligations. NTT disclosed only its total consumption for 2025, equivalent to the annual consumption of about 450,000 households. NTT refused to give figures for individual data centres, citing the trade secrets exemption in the EU Energy Efficiency Directive.

German law contains no such exemption. The Federal Office for Economic Affairs and Export Control (BAFA), which collects the data, said it has not agreed with anyone to withhold data on trade secret grounds. The GDA justifies the companies’ reticence by saying that data centres are highly sensitive sites. A legal opinion commissioned by the organisation AlgorithmWatch finds that consumption data are protected trade secrets only in exceptional cases, SWR reported.

The government is also preparing an amendment that would exempt trade secrets from the publication obligation. The change would meet the wishes of Bitkom and the GDA. If the Bundestag passed it, companies could classify their consumption as a trade secret more easily than they can now, SWR said. The EU directive already contains a similar clause. SWR reported that Die Zeit had found the clause was taken almost word for word from a lobbying organisation. Zeit said the clause may breach the Aarhus Convention, which requires environmental information to be publicly available.

What this is based on

1. Wie Betreiber von Rechenzentren Angaben zum Energiebedarf verstecken

   Publisher: www.heise.de · published 8 November 2026 at 08:14 (UTC+02:00) · retrieved 9 November 2026 at 11:23 (UTC+02:00) · discovery channel: web

2. Ressourcenverbrauch vieler Rechenzentren nicht öffentlich einsehbar

   Publisher: www.tagesschau.de · published 6 November 2026 at 07:04 (UTC+02:00) · retrieved 9 November 2026 at 11:23 (UTC+02:00) · discovery channel: web

Udgam Labs said in a blog post on 7 November that its Shruti speech-to-text models now cover all 22 languages in the Eighth Schedule of India’s Constitution. Each language has its own model, and every model also recognises English.

The models are built on Cactus Whistle, a speech model from Cactus Compute. It is an open-weight model with 55 million parameters. Udgam Labs added vocabulary for each language and fine-tuned all the model weights on public speech datasets. The weights are quantised to 4 bits, so each model file is 29.5–29.7 MB.

The models write speech in each language’s own script. They often leave English words in Latin script, as many people do when they write: ‘मेरा order कब आएगा’.

Gains depend on how errors are scored

The company measured accuracy by word error rate, the share of words recognised incorrectly, so a lower figure is better. The main test was Indic DiarBench from Sarvam AI and AI4Bharat. Its real conversations were not in any model’s training data, the company said.

Udgam Labs said 11 Shruti models make fewer errors than IndicConformer for the same language when an English word counts as correct only if written in Latin script. In Gujarati, for example, Shruti scored 28.5, SraVaani-1.0 32.4 and IndicConformer 44.9.

If English words must be written in the language’s own script, IndicConformer or SraVaani-1.0 is more accurate in most languages. On read speech, the larger models are clearly more accurate in most languages. On the FLEURS test, for example, IndicConformer Punjabi scored 16.9 and Shruti Punjabi 29.3. In conversations, between a quarter and more than half of words are recognised incorrectly, depending on the language.

Some tests are small: in the conversation test, 11 languages have fewer than 100 speaker turns. For seven languages, the company presents no comparison it considers reliable. All figures are Udgam Labs’ own measurements.

The Hindi model file is about 100 times smaller than OpenAI’s Whisper large-v3. In the company’s Hindi tests it made fewer errors; on the Monsoon test, for example, the scores were 20.46 and 33.29. The company ran Whisper without its temperature fallback, which caused repetition loops. Shruti was more accurate even when clips stuck in repetition loops were removed, the company said. In English, Whisper is clearly better. Hindi-only models about 16 times larger are much more accurate, according to the company.

Limitations and deployment

The company has not tested the newer models in noise or with music, nor most languages with microphone input. In heavy noise, the Hindi models sometimes repeated the same phrase. Native speakers have not checked the transcripts for the 16 newer languages.

In the company’s measurements, 21 newer models ran on a five-year-old phone with 6 GB of memory. They transcribed 8.6–12.6 times faster than real time and used 64 MB of memory.

ONNX packages are also available, for example for browsers and Android. None of the packages passed all six of the company’s comparison rules. Nine failed the test, but the company released them anyway and disclosed the deviations. In some cases, a deviation meant fewer errors than the model running on Cactus’s needle engine.

The models are on Hugging Face under the Apache-2.0 licence, and runnable examples are in the company’s GitHub repository. The licence does not set terms for the training data, and no licence is named for two datasets used for Shruti Assamese. The models run on the needle engine without a key or account, but the first download requires an internet connection unless the model ships with the app. By default, the engine’s Python package sends anonymous usage statistics; users can switch this off with the environment variable NEEDLE_TELEMETRY=0.

What this is based on

1. github.com

   Publisher: github.com · source publication time unknown · retrieved 9 November 2026 at 11:23 (UTC+02:00) · discovery channel: GitHub

2. Shruti, 22 small speech-to-text models that run on your device

   Publisher: udgamlabs.com · published 7 November 2026 · retrieved 9 November 2026 at 11:23 (UTC+02:00) · discovery channel: web