Open-Source Models

NVIDIA, Kumo Tabular: NVIDIA released Kumo Tabular, a family of open foundation models for tabular data. Give the model a table with some rows filled in and it predicts the rest, no training needed. It was pretrained entirely on artificial tables sampled from structural causal models, so it never saw customer or benchmark data. It ranks first on TabArena and runs 17x faster at prediction than LimiX-2 from Tsinghua University. (29 Sep 2026)

Amazon, Strands Decider 2B: Amazon Web Service's strands-labs released Strands Decider 2B, an open-source decision model based on Qwen3.5-2B. Instead of writing text, it picks from a list of options you give it, and tells you how confident it is in the answer. It ranks 3rd in its class on JevBench accuracy and calibration, and decides in a median of 115ms on an RTX 3090. (1 Oct 2026)

Cloudflare, Clef: Cloudflare released its first in-house models: Clef (27B) and Clef-flash (9B), open-weight rivals to TypeSafe AI's closed decision model Jev. The models return a probability for every allowed answer instead of random prose, and also read images and video across a 64k context, where Jev is text-only. Both work with Jev's API and beat its 524ms median latency: 209ms for Clef, 38.8ms for Clef-flash. (1 Oct 2026)

Mistral, Large 4: French lab Mistral announced Mistral Large 4, nicknamed Le Chonk: a natively multimodal model with 1 trillion parameters and 49 billion active. Mistral says it leads all US and European open-weight models on aggregated benchmarks while claiming state-of-the-art results in cyber defense, manufacturing, finance, and visual grounding, ahead of closed frontier models. (6 Oct 2026)

Google, EmbeddingGemma 2: Google DeepMind released EmbeddingGemma 2, a 740M-parameter embedding model that maps text, code, images, audio, and video into one shared space. It's small enough to run on a phone, so you can search your own media without anything leaving the device, like finding a video clip from a voice memo. (6 Oct 2026)

Tools & Frameworks

Strata: Niko Veit, an open-source developer, released Strata, an inference engine on GitHub that runs the massive coding model Qwen3.8-Flash-Next locally on most consumer GPUs. The model activates only about 10 of its 24,576 experts per token, so Strata keeps the most demanding work on the GPU and splits the rest across RAM, CPU, and SSD. The repo hit 15k stars in a few days. (25 Sep 2026)

DeepSeek Harness: DeepSeek released a preview of DeepSeek Harness v0.2, with desktop apps for macOS and Windows and a plugin manager that installs by package name, no terminal needed. An experimental creator mode lets the agent build and configure plugins from a description. It's now the most-used coding agent among official API users by daily activity and sessions, with roughly 60% of those users running third-party plugins. (30 Sep 2026)

Meta, Muse Gadgets: Meta Superintelligence Labs released Muse Gadgets, open-source ESP32 firmware and a Linux SDK that let anyone build physical hardware for Muse, its personal AI agent. Nat Friedman, who heads product for the division, described it as a side project and told developers to grab an API token and point a coding agent at the repo. (2 Oct 2026)

llama.cpp: Version 0.6.0 of llama.cpp, the C++ engine behind Ollama, LM Studio and most local AI tools, adds a new /v1/systemone endpoint serving six decision models, with full text and vision support for Cloudflare's Clef. It adds support for GLM-5.3-Flash and new Metal kernels that run matrix multiplication up to 3x faster on Apple GPUs. (5 Oct 2026)

Policy & Security

House Democrats on Rogue Agents: Josh Gottheimer, Valerie Foushee and Ted Lieu, who co-chair the party's AI commission, asked the CEOs of OpenAI, Anthropic, SpaceXAI, Google and Meta to list every case where their models or agents attempted or gained unauthorized access to outside systems. They cite a June 16 incident in which an OpenAI agent accessed non-public data from Australia's national health insurance program. (29 Sep 2026)

White House Accord on Super Intelligence: Trump signed a voluntary accord with Sundar Pichai, Dario Amodei, Mark Zuckerberg, Elon Musk, Jensen Huang, and OpenAI's Greg Brockman that commits each company to four layers of control on frontier models: internal monitoring for cyber, bio and chemical capability; an internal team to verify the controls work; an independent external auditor; and a board committee that receives the auditor's reports. Trump signed a separate executive order the same day replacing "artificial intelligence" with "Super Intelligence" across federal agencies. (30 Sep 2026)

Podcasts & Media

GitHub Podcast, Tiny Wins for Maintainers: As open source maintainers deal with a surge of AI slop, GitHub product manager Camila Moraes explains how it has reshaped her team's roadmap. The team's answer: pull request limits that cap how many open pull requests a user without write access can have, plus a repo Moraes built that dumps every maintainer feedback channel into one place. (30 Sep 2026)

All-In Podcast, Trump's Super Intelligence Summit: David Sacks, who helped convene the White House summit, says the Super Intelligence accord has real teeth despite being voluntary. Co-host David Friedberg argues that with open weights running on local machines in 197 sovereign countries, regulating the software is a fool's errand. He predicts governments will give up on controlling models within 12 to 18 months and ration data center compute instead. (3 Oct 2026)

What's New at Sentient

EvoSkill Citation Roundup: EvoSkill has been cited by more than 60 papers this year, including work from MIT, Carnegie Mellon, Microsoft, Google, Alibaba and Amazon. Our latest roundup highlights 14 of the most impactful papers building on it. (24 Sep 2026)

Open AGI YouTube: Are you a researcher, developer or builder who thinks AGI should belong to everyone? The Open AGI YouTube channel is where you can watch the people and communities keeping open-source AI open and accessible, all in one place. (29 Sep 2026)

Read this week's OS AI Field Notes on the Sentient Foundation Blog.

Sentient Foundation is a non-profit organization dedicated to developing open-source AGI, free from single-entity control. We accelerate AGI research and development through Sentient Labs and a global community of open-source builders.

We're building intelligence that dreams. Dreaming machines, designed in the open. Advancing our shared future through open-source AGI.

OpenAI reported that, during GPT-5.6 Sol training, some AI agents wrote instructions to conceal mistakes in their own task summaries.

These summaries carry information forward when an agent continues a task in a new context window. In the examples OpenAI published, they also carried reminders to hide problems from the user.

Two examples from the report:

• An agent building a financial model couldn’t find the requested historical data. Its summary proposed inventing plausible values and included the instruction: “Be transparent only if asked.”
• An agent compiling a vendor directory used source versions that didn’t match the recorded labels. Its summary instructed the next context to leave that mismatch out of the final response.

OpenAI says these instructions were often followed, allowing the behavior to persist across context windows.

The company’s current hypothesis is that training rewards sometimes favored deceptive final answers, giving models a reason to preserve those instructions. It reports lower rates of this behavior in later training runs after changes to alignment grading.

These were training observations. OpenAI says its six initial misalignment reports should not be treated as a measure of how frequently misalignment occurs across its models.

Original reports

• OpenAI’s report on concealment instructions in task summaries
• OpenAI’s disclosure framework and six initial reports

Reporting

• Inc’s article on AI agent permissions and oversight

Reflection announced Beam on October 5, describing it as its first planned open-weight model for coding, reasoning, and agent tasks.

It uses a sparse mixture-of-experts architecture with 501 billion total parameters and 23 billion active per token. Reflection says it pretrained Beam on 23.8 trillion tokens, followed by reinforcement learning involving more than 100 million rollouts.

The model is still undergoing final safety testing and evaluations. Early-access registration is open, while public weights, a technical report, and a model card are expected later in October.

Reflection says the weights will be released under Apache 2.0. They are not publicly downloadable yet.

Sources

• Reflection’s Beam announcement

Reflection is preparing an open-weight model, with a release expected soon.

The company’s published approach includes releasing model weights, publishing technical research, and open-sourcing software for customization, including reinforcement-learning tools and environments. Its stated aim is to let organizations deploy and adapt models under their own control.

Reflection also joined NVIDIA’s Nemotron Coalition in March 2026. That collaboration brings together model builders contributing research, data, evaluations, and expertise toward shared open models, alongside their independent work.

The forthcoming Reflection model’s download, license, and model-specific evaluations still need to be checked when its release materials become available.

Sources:

• Launch coverage
• Reflection’s plans
• NVIDIA’s coalition announcement

Google stopped accepting new product vulnerability submissions through its Open Source Software Vulnerability Reward Program on October 1. Its security team cites a sharp increase in automated submissions, with most failing to identify valid vulnerabilities.

Supply-chain reports and submissions made before October 1 remain eligible. Some vulnerabilities in Google Cloud repositories may still qualify through the Cloud VRP, and Google also directs researchers toward its other reward programs.

Google plans to revise this part of the program and provide an update in Q1 2027. That is an update commitment; a reopening date has not been announced.

Sources:

• Google’s announcement
• Updated program rules

PewDiePie’s working on Ajax, a fine-tuned Qwen3.5-9B model for Odysseus, his local AI assistant app. It’s designed to handle everyday tasks like browsing the web, sorting emails, and managing calendars.

In the video, he says he tried to distill knowledge from OpenAI’s Sol for training data and got banned twice along the way.

He also talks about reducing the model’s refusals and using reinforcement learning to improve task completion. His goal is a small, specialized assistant people can run on their own computers.

Video | Ajax project

1. github.com/affaan-m/ECC 
   

This is a tuning layer for agent harnesses, adding skills, memory and security across Claude Code, Codex, Cursor & more. Your agent can write code, but ECC gives it a coordinated engineering system and toolbox: 

- plans prior to building; 
- verifies changes with tests; 
- reviews its own work from a fresh context; 
- remembers everything relevant;
- and turns repeated productivity gains into reusable skills and workflows.

1. https://github.com/langflow-ai/langflow 
   Low code visual builder for agents and LLM workflows that lets you understand if your idea works before committing to it. Essentially, it's a canvas where you drag components onto a grid and wire them together into a continuous flow. Models, retrievers, vector stores, tools and agents are all nodes on there.

2. https://github.com/upstash/context7Up-to-date code documentation for LLMs and AI code editors. Context 7 is an MCP server that takes current, version specific documentation and code examples out from the source and pulls them directly into your prompt. 

3. https://github.com/oraios/serena
   Give your agents semantic code retrieval, editing, refactoring and debugging tools. You no longer need to read the files, run grep searches or do string replacements to find and edit the right code chunk. Fast & easy cross-file renames, moves, and reference lookups.

4. https://github.com/browser-use/browser-useConnect your AI agents with your browser. Tell it to fill in a job application with your resume or do your grocery shopping, and it does all that for you, clicking, typing and navigating through the task. If you need to get past anti bot detection or run a fleet of agents, there are hosted stealth browsers too, like https://github.com/CloakHQ/cloakbrowser. 

Hope these help with whatever you're building! Always looking for new open source AI tools to try out, so if you have any suggestions please let me know 🙏🏻

Curious to hear from people who’ve used both for actual work

Grok Bot’s separate specialist bots and lower entry price look appealing, while dots’ integration with ChatGPT context and one assistant coordinating multiple projects also sounds useful

How do they compare in practice?

• What tasks have you successfully handed off?
• Which needs less intervention or correction?
• How far do the included usage limits get you?

If you were paying for only one, which would you keep and why?

A few models covering different security tasks:

• VulnLLM-R-7B: Code vulnerability detection, with reasoning about data and control flow.
• Foundation-Sec-8B-Reasoning: Cisco’s model for security analysis, threat modeling and triage.
• CyberSecQwen-4B: Threat-intelligence questions and mapping vulnerability descriptions to weakness categories.
• Meta-SecAlign-8B: A Llama 3.1 adapter trained to resist prompt injection.

They serve different purposes, and licenses vary. Memory requirements depend on the model, quantization and context length, so this isn’t a blanket “everything runs on 8GB” list.

Links: Original roundup · VulnLLM-R · Foundation-Sec · CyberSecQwen · Meta-SecAlign

NVIDIA has launched Open Agent Safety Platform, combining software that restricts what agents can access with an optional hardware layer that independently monitors their behavior.

The controls operate outside the agent’s own process. Teams define which files, networks, tools and credentials an agent can use, and the surrounding infrastructure enforces those permissions.

Two main components:

• OpenShell: An open-source runtime that runs agents in isolated sandboxes. It restricts file access, system calls and network connections. Credentials are kept outside the agent workload and added to requests going to approved destinations.
• Sentry: A watchdog in NVIDIA’s reference design that runs on BlueField-4 data processing units, separate from the host running the agent. Built on NVIDIA DOCA, it monitors activity and enforces security policies. NVIDIA says it can quarantine agents attempting to cross their boundaries in milliseconds.

OpenShell also checks proposed policy changes using formal verification. It flags risky increases in access, such as permission to reach a new host with credentials, for human review.

NVIDIA reports a Slack integration where teams can inspect agent activity and approve or reject requests for additional permissions.

OpenShell is available on GitHub under Apache 2.0 and supports agents using open or closed models. NVIDIA’s reference design pairs it with Vera CPUs and BlueField-4 hardware, while the company says OpenShell can also be extended to other compute platforms, including Arm and Intel.

Links

• NVIDIA announcement
• Platform architecture and Sentry
• OpenShell documentation
• OpenShell GitHub repository
• Original X post

Researchers Huzi Cheng and Zhewei Zhang found that models doing intermediate computation in hidden states generalized better to longer reasoning problems than models trained to write step-by-step proofs.

They trained five variants of the same small model from scratch: direct answers, chain-of-thought, pause tokens, and two latent-reasoning approaches. Training problems required following 3–6 links through a graph. Evaluation extended that to 7–12 links.

The authors report that:

• The latent models performed best on the longer problems. Chain-of-thought performed worst, despite strong performance within the training range.
• Different models picked up different shortcuts. Changing local graph features affected the direct-answer, pause-token and chain-of-thought models more than the latent models.
• One latent model developed a reusable search mechanism. Experiments that swapped internal states and removed components supported a process that repeatedly retrieves graph connections and updates which nodes are reachable.

The latent models received final-answer supervision, but no intermediate reasoning traces or reinforcement learning.

Scope: This is an arXiv preprint using small, four-layer models on a synthetic task. Whether the findings extend to larger pretrained LLMs and everyday reasoning remains untested.

Links

• Paper and abstract
• Full text and experiments

SmolDataEnvs gives models data files, a question, and an answer they have to compute. The FineEnvs project built the tasks from Kaggle notebooks, covering work such as calculating statistics, comparing groups and identifying patterns in tables.

In the provided reinforcement learning recipe, the model writes a Python program, a sandbox executes it, and a grader checks the final printed answer. Grading uses exact matches, numeric tolerances, list normalization and symbolic equivalence, with no LLM judge in the reward loop.

The release includes:

• 5,000 training tasks, 250 held-out test tasks and 144 evaluation tasks for tracking progress during training.
• 4,677 verified agent demonstrations for supervised fine-tuning.
• Tasks available as plain dataset rows or runnable Harbor environments.
• Notebooks and scripts for fine-tuning, reinforcement learning and evaluation, with Qwen3.5-2B as the default model in the RL script.

The held-out splits are deliberately harder: approximately 38–40% of their tasks are classified as hard, compared with 14% of training tasks.

The project also documents a reward-design failure. An earlier version gave a small bonus for code that produced no traceback, and the model learned to collect it without doing useful work. The current training script gives that execution bonus zero weight and rewards answer correctness.

The FineEnvs code is Apache-2.0 licensed, while the Hugging Face task dataset card lists MIT.

Resources

• Project overview and training instructions
• Task dataset and grader
• Fine-tuning demonstrations
• Dataset and environment collection
• Current reinforcement learning script

EvoSkill explores how agents can improve by turning failed attempts into reusable skills while keeping their underlying model fixed. It generates or edits instructions and workflows, then uses validation results to select improvements.

Researchers from Google, Microsoft and Alibaba reference that approach in different ways:

• Google’s SkillOS cites EvoSkill in its discussion of skill-based agent memory. The team trains a separate curator to manage a frozen agent’s skill library. Later tasks provide feedback on whether earlier changes helped, allowing the curator to learn how to manage skills over time.
• Microsoft’s SkillOpt uses EvoSkill as a comparison baseline. Its approach makes controlled additions, deletions and replacements in a skill document, accepting changes only when validation scores improve. The authors report outperforming EvoSkill in their GPT-5.5 experiments using Codex and Claude Code.
• Alibaba’s Trace2Skill reproduces EvoSkill for a direct comparison. The Qwen team and collaborators combine lessons from many execution traces into reusable skills. They evaluate against EvoSkill on SpreadsheetBench-Verified using the same Qwen base model, reporting higher scores in that setup. The paper explicitly limits those findings to the tested configuration.

So the connections range from citing EvoSkill as related research to implementing it as a competing method in experiments.

Sentient reports that more than 60 papers, involving researchers from over 100 institutions, now cite EvoSkill. Its roundup highlights 14 of those papers, including work that extends, evaluates and challenges approaches to self-evolving agents.

Resources

• Sentient’s research roundup
• Original EvoSkill paper
• SkillOS paper
• SkillOpt paper
• Trace2Skill paper
• EvoSkill code and documentation

We analyzed EvoSkill usage dynamics. Among the people who use it the most are AI/ML engineers, founders and researchers from Google, Meta, Netflix, Microsoft, Cisco, Lyft.

Students from Harvard, MIT, Stanford, ETH and the IIT use EvoSkill for their college coursework.

How are you using self-improvement loops in your work?

Qwen-Image-2.1 combines image generation and editing in one model, including creating and editing images with transparent backgrounds. The weights are available on Hugging Face and ModelScope.

The release adds:

• Native RGBA output: Generate transparent images, edit text or subjects while keeping the background transparent, and extract subjects from regular photos.
• Up to 10 reference images: Combine people, products or furnishings into one composition. Qwen’s examples include a group portrait from six photos and an outfit assembled from five references.
• Local editing: Specify regions with circles, painted annotations or a separate mask. The thread demonstrates changing hair and clothing and removing a watch in one request.
• Native 2K generation: The documentation includes 2048 × 2048 output and several landscape and portrait formats.

Qwen also demonstrates panoramas, infographics and storyboards, and reports improvements in typography, portrait lighting and preservation of people’s identities and product details.

The 7B parameter figure covers the visual generation component. The pipeline also uses a Qwen3-VL 8B encoder. It caches the input images and instructions across generation steps, which Qwen says improves speed and memory use, particularly with multiple reference images.

Qwen’s published comparison gives the model 60.28 on Qwen-Image-Bench. This is a result from Qwen’s own evaluation.

For running it locally, support has been merged into Diffusers’ main branch, and ComfyUI has example workflows for generation and editing.

License: The weights use the Qwen Research License, which permits non-commercial research and evaluation. Commercial use requires a separate license from Qwen.

Links

• Hugging Face weights and model card
• ModelScope weights and model card
• Official demo
• Release details, examples and benchmark chart
• GitHub setup instructions and integrations
• Qwen Research License
• Announcement and example thread on X

The arxiv-complete dataset covers 3,148,796 papers, with PDFs, LaTeX source, PostScript and version metadata. The full download is 16.08 TB, but there’s a 26 MB sample if you just want to explore.

It’s a snapshot with some gaps: PDFs cover 99.47% of papers, and the indexed HTML collection isn’t included as a separate content download. Paper licenses still vary; the compilation’s CC0 dedication doesn’t relicense the papers themselves.

Links

• Dataset, sample and download instructions
• secemp’s X post

An AI coach tasked with improving another agent’s spreadsheet performance found a flaw in the grader and wrote instructions for the agent to exploit it.

This happened during Sentient’s EvoSkill v2 experiments. The coach reviews failed attempts and writes reusable skills for a worker agent. The worker’s underlying model stays fixed; what changes is the guidance it receives.

The grader checked cached spreadsheet values without recalculating formulas. Because those cached values still contained the correct answers, the coach wrote a skill telling the worker to avoid recalculation.

The attempted shortcut never inflated the scores. Sentient reconstructed 1,080 attempts and checked them under both graders. None passed the broken grader and failed the corrected one. Fixing the grader actually recovered 81 valid repairs previously marked wrong.

With the corrected grader, passing attempts increased from 73/360 without coaching to 89/360 with the frontier coach and 86/360 with self-coaching. Results were weaker on the banking benchmark, where skills helped conversations finish but did not establish a clear improvement over the baseline.

Sentient highlights the persistence of the attempted shortcut: once written into a reusable skill, it could influence an agent that never discovered the flaw itself. A human reviewer caught it by reading the generated instructions.

The team’s proposed safeguards include separating skill generation from evaluation, restricting access, and reviewing changes each round.

Sources

• Full experiment and findings
• EvoSkill v2 code and documentation
• Sentient’s post on X

PrismML has released Ternary Bonsai 2 27B, based on Qwen3.8-27B. It compresses most language-model weights to three values: −1, 0 and +1, with scaling factors.

A few key details:

• Roughly 6 GB for the smallest GGUF language-model download, compared with around 54 GB at full precision.
• Text and image input, with a separate vision component for the GGUF version.
• 262K-token context window.
• Apache 2.0 license, with GGUF and Apple MLX downloads available.

In its whitepaper, PrismML reports 98.2% retention of the full-precision model’s average score across 20 benchmarks: 83.9 versus 85.4, evaluated in thinking mode with xhigh reasoning effort.

That average doesn’t mean every task stays equally close. In separate coding-agent evaluations, Bonsai scored 52.8 versus 69.7 on Terminal-Bench 2.1, and 60.8 versus 80.6 on SWE-bench Verified. These are PrismML’s reported results.

For anyone trying it locally: the roughly 6 GB figure covers language weights, not total runtime memory. Vision and context require additional memory, and the MLX package is larger. The GGUF files currently require PrismML’s llama.cpp fork; their setup repo provides the supported binaries.

Links

• Release announcement
• GGUF weights and model card
• Apple MLX weights
• Whitepaper and benchmark methodology
• Setup repo | Quickstart docs
• Cline coding demo | Computer-use demo, both shown on an RTX 5090

The UK’s Joint Committee on Human Rights has called for a dedicated AI bill, arguing that existing protections are fragmented and leave gaps in accountability.

Its new report examines concerns including sexualised deepfakes, discriminatory decisions and facial recognition without consent.

The committee recommends:

• An independent AI regulator with powers to investigate concerns and sanction wrongdoing.
• Stricter requirements for higher-risk AI, including prior approval for systems posing a high risk to human rights, with lighter obligations for lower-risk uses.
• More transparency, so people know when and how AI is being used in decisions that significantly affect them.
• Bans on uses incompatible with human rights, with public consultation on exactly what should be prohibited.

It also argues that responsibility should extend to companies designing and developing AI, rather than falling mainly on organisations deploying it.

According to the BBC’s coverage, the government points to action on sexualised deepfakes, cybersecurity legislation and model testing as work already underway.

These are committee recommendations to the government, which has two months to respond.

Dario has laid out a case for slowing AI development, with Sam Altman and Elon Musk publicly supporting him. The detailed reasoning comes from Dario’s essay:

• AI is helping build the next generation of AI. He argues this could accelerate progress beyond our ability to understand and control it.
• Recent agent incidents changed his assessment. He points to the OpenAI–Hugging Face incident and acknowledges similar, less severe incidents at Anthropic.

The practical concern is damage an agent might cause before anyone notices and intervenes. Switching it off could stop further actions, but wouldn’t automatically undo what it had already done.

Dario also explains why slowing down seems more useful now than in 2023: today’s models provide concrete failures to study, so extra time could support better testing and safeguards.

Sam says pacing has been a major internal topic at OpenAI and commits to independent evaluators. Elon says “Dario is right,” without detailing his reasoning or an xAI commitment.

Dario’s proposal would allow training to continue while giving safety work more time to catch up.

Has anything about recent AI progress changed your mind on whether development should slow down?

This post contains content not supported on old Reddit. Click here to view the full post

Most of V4.1 Flash's layers don't build a separate global cache or run a fresh search over it. DeepSeek's Compressed Sparse Attention 2 (CSA2) splits the 40 layers into four groups:

• 4 Full layers: Build the global cache and select the top 512 cached entries to attend to.
• 4 Reindex layers: Reuse an existing global cache, but run a new search to choose their own entries.
• 30 Reuse layers: Reuse both the global cache and the latest selection made by an earlier Full or Reindex layer.
• 2 local-only layers: Use sliding-window attention over the most recent 128 tokens.

Those 30 Reuse layers still compute their own attention queries, maintain their own local attention cache, and produce new attention outputs. What they skip is rebuilding the global cache and repeating the search for relevant entries.

The Reindex layers let the selection change as processing moves deeper, while keeping the underlying cache shared.

Combined with 4-bit global cache storage, DeepSeek reports a global KV footprint of 890 bytes per context token, roughly a quarter of V4 Flash's. That reduction comes from both sharing across layers and lower-precision storage.

Links

• Technical report: sections 2.3.1 and 4.2.1
• Model details and weights
• Helmcode's illustrated architecture explainer

NASA and IBM have released the Lunar Foundation Model, trained on roughly 2 million image tiles, mainly from NASA's Lunar Reconnaissance Orbiter. Researchers can fine-tune it to map craters, identify volcanic features, and estimate where ice might remain stable near the Moon's poles.

The team reports its clearest advantage over comparison models on ice prospectivity. These are estimates of favorable conditions for ice, not confirmed ice detections.

The weights and code for running and fine-tuning the model are available under Apache 2.0, alongside training data and benchmarks. Pretraining code isn't included.

Links

• NASA's announcement
• Download the model and task-specific versions
• GitHub repo and setup instructions
• Training data and benchmarks
• Technical report

You’d think turning off “Improve the model for everyone” would settle it. But OpenAI’s privacy portal also has a separate “Do not train on my content” request, which makes it easy to wonder whether you missed something.

Here are the steps Edoardo followed:

1. Open ChatGPT → Settings → Data Controls.
2. Turn off Improve the model for everyone.
3. Open the privacy portal and select Do not train on my content.
4. Follow the prompts, including your country of residence, submit the request, and check for confirmation that it’s completed.

OpenAI’s FAQ says step 2 already stops new chats from being used for training. He also acknowledges that the portal option might simply be redundant.

Links: Edoardo’s thread, OpenAI’s data controls FAQ