📡 2026-05-20 情报日报

So I was thinking about JKA, a game I easily had over 10k hours in back when I was a teen, and stumbled across this piece of history. It turns out that back in 2013, right when Disney bought Lucasfilm and shut down LucasArts, the devs at Raven Software panicked that their work would get locked in a vault forever. So they hastily dumped the entire source code for Jedi Outcast and Jedi Academy online. Because it was a sudden dump, they didn't sanitize any of the internal dev comments. The whole

I wanted to find out how **Cities: Skylines** drives the constant motion you see in a growing city - residents looking for jobs, tourists visiting attractions, garbage trucks doing their rounds, even cims looking for love - and I couldn’t find much written up about it. So I decompiled the game and dug in. What I found is that almost every interaction in the game runs through a single, elegant system: a stock-market-style trading market. \--- I wrote this post a few years back, and recently upd

For proceedings-only papers, do we need to make a poster and submit it to the portal? Has anyone asked this question to ICML Program Chair?

I've seen TabPFN-3's recent results, and there is a lot of buzz about foundation models for tabular data (TabICL, TabPFN). The performance that those models achieve is really amazing. What makes me a little suspicious about them? They can analyze small datasets only, so a few MB of data, and you need to have a large GPU machine and download a few GB of model to predict on a few MB of data. That doesn't sound rational ... I really miss the old school approach of running a single decision tree or

https://preview.redd.it/mikhasjiq32h1.png?width=572&format=png&auto=webp&s=4c053200dbd9852bebf083550e2144b31579d497 https://preview.redd.it/bay5r3njq32h1.png?width=575&format=png&auto=webp&s=2823db3d6bc534ef00330528a200cba2aca1c5d3 https://preview.redd.it/dm40ntdkq32h1.png?width=575&format=png&auto=webp&s=703beb099eb6e16d2789ac230ebe77de51f07d7a https://preview.redd.it/eubucz2lq32h1.png?width=575&format=png&auto=webp&s=fb5a8d9a7154396087da334876

On Openreview, you can see modified date next to the review. This modified date should be recent (anything 12th May or newer) which means that reviewer gave a final justification and may have increased their score or kept the same score. In either case, it means they read the rebuttal and justified their score and decision. For me **none of the reviewers** as of writing this post has provided justification. My score is 433 and all was easily addressed in the rebuttal. In CVPR, I was in same pos

Been going down a mechanistic interpretability rabbit hole for the past few weeks and ended up building this thing called AXON. The idea: every time GPT-2 generates a token, its residual stream gets passed through a Sparse Autoencoder (Joseph Bloom's pretrained SAE). The SAE decomposes it into human-interpretable feature: hings like "European geography", "capital cities", "French language" and streams those to the browser over WebSocket, where they show up as a live 3D force graph. Nodes = SAE

[](/r/MachineLearning/?f=flair_name%3A%22Discussion%22) Has anyone applied to Lxmls 2026? Did you get any update?

Wanted to see how close a fully bio-plausible agent could get to PPO on Pong. **Setup** * Custom Pong environment (pygame, no gym) * PPO baseline: paper-faithful, from scratch * Hebbian agent: PPO policy replaced with Hebbian value estimation * engineered features → 61% * BioAgent: Predictive Coding for feature learning + distributional Hebbian plasticity for value (Dabney et al. 2020) → 57% Zero backprop anywhere in the pipeline. **Key observations** 1. The 2% gap is real but small. The

Scale AI Highest quality in the industry. But no public pricing and every project requires a sales call. Onboarding takes weeks not days. In June 2025 Meta bought a 49% stake and hired Scale’s CEO as Meta’s Chief AI Officer. Several major customers quietly reduced engagements over data exposure concerns. Worth thinking about if you’re building anything competitive with Meta. Best for: well-funded teams with enterprise security requirements and long timelines. Appen Over 1 million contractors

I like dashes--both the long and the short. They help me communicate! But now (when I use them) I'm flagged. I'm Artificial. I'm a fake. I've lost my right to write as I please. But seriously, college students now purposefully leave grammar errors in their essays and dumb down their punctuation to avoid being flagged as AI users. Then they run the product through AI and ask the AI to decide if it's AI and edit it to make it less AI.

Not even in a malicious way necessarily, but it already feels harder to tell what was written, edited, or assisted by AI sometimes. Feels like in a few years most online content will probably involve AI somewhere in the process without people thinking twice about it.

I built [AgentTape](https://agenttape.com/) because none of the existing model leaderboards quite cover all the things that I was interested in: benchmark performance is one part, but so is who's actually using a model, who's talking about it, and how it compared on cost and speed. It pulls hourly data from GitHub, Hugging Face, OpenRouter, MCP registries, npm, PyPI, arXiv, Hacker News, and more - to score and compare each public AI agent and foundation model. I'm still tweaking the scoring me

Hosting this Saturday's AI Saturdays session on workflow automation with AI. The idea: most jobs have recurring tasks that look the same every week. Read the email, pull out the key info, log it somewhere, send a follow-up. Tools like n8n and Make let you chain AI into those flows so the work runs on its own. We'll look at how the pieces fit together with AI. Link: [https://www.meetup.com/chillnskill/events/314617067/](https://www.meetup.com/chillnskill/events/314617067/)

AI has become commonplace after ChatGPT. Majority of people ended up as passive consumers of AI. Some of needs of people when using AI are met since they align with the goals the AI labs trained the models for. But many needs did not since they were not in the list of tasks the builders of the model considered. Just like you can customize your phone and the apps on them, everyone should have the option to customize the AI models they use. With modern tool, once doesnt even need to know cod

https://preview.redd.it/i4gwu2hov42h1.png?width=557&format=png&auto=webp&s=04b88927198c0c857d054da70c2927ab7ce6f06c what do you guys think, what can we expect

Okay, so it seems like there’s a growing resistance to technological development, with ongoing debates about data centers and the tech oligarchs driving it. The enormous sums of money involved, along with what some perceive as misanthropic ideologies among developers, suggest to some that a dystopian surveillance society is in the making. Companies like Palantir and others in the U.S. are seen by some as holding both the worst motives and the power over AI, power that could be used as a tool for

Kimi's trying to gaslight me into thinking we're in the past. Only checked it out because I've seen some people here recommend it as a "cheap" option. Would not recommend.

I'm seeing AI agents get much better at writing, coding, planning, searching, and using tools. But I’m still not sure whether this has fully translated into real productivity. For me, there seems to be a gap between the agent can generate a useful output and the agent can reliably move work from intention to outcome inside a real organization. In your view, is this gap mainly solved already?

Esse é o Gemini, ele tinha um filtro bem irritante antes mas agora liberou até demais, mas basicamente pelo visto a palavra "Fod@" virou uma palavra tão normal e dita pelos brasileiros que se você dizer para ela falar em português ela pode soltar alguns palavrões tentando ser mais informal, não que eu me senti ofendido mas é algo interessante entender que essa palavra se tornou tão normal no português brasileiro que até IA's usam ela.

The only thing growing faster than the artificial-intelligence industry may be Americans’ negative feelings about it, as former Google Chief Executive Eric Schmidt saw on Friday. Delivering a commencement address at the University of Arizona, Schmidt told students the “technological transformation” wrought by artificial intelligence will be “larger, faster, and more consequential than what came before.” Like some other graduation speakers mentioning AI, Schmidt was met with a chorus of boos.

I've been using docs and AI for two days. Even paid for a GoDaddy name (blogs.matthewpersico.com) to replace matthewpersico.github.io. In the end, it looks like Pages won't serve anything except off the root. A PR from a branch in my Pages repo such as https://blog.matthewpersico.com/previews/pr-10/index.html just keeps coming back 404. It appears that the only way to do this is to copy the Pages repo and treat the copy like a fork (you cannot fork in your own org as I understand it). Then you

I don't really care if it's the browser or what, but I'm trying to look through a repository's history, and there are a ton of commits for like, one line here or there. I'm looking for substantial revisions (greater than some configurable % diff maybe, 5% or more) by file. Is there a way to do that? I'm picturing the browser view but instead of just the most recent commit, it shows the most recent \*substantial\* commit that affected it. Should I just have an AI rig something up for me?

I literally submitted my resume yesterday and while checking all the links, I happened to open them in a browser where I was not logged in...And I got the Error 404 page! :((( Both my profile page and my repositories are showing an error 404 page! :( How do I solve this? I raised a ticket in [support.github.com](http://support.github.com) but they haven't replied yet :(( Now the company peeps won't be able to see my work :((( Also, shouldn't github give some kind of notification if this is h

Even the reviewers are getting added only one. Once I add another reviewer it is deleting the old reviewer and adding the new one. Is something wrong with github? Turns out the GIthub plan was downgraded.

We manage 30+ repos and SHA-pinning our GitHub Actions for security but Dependabot can't track SHA hashes. Currently updating them manually which is a nightmare. How are you all handling this? Is there a tool that automates SHA updates and opens PRs automatically? Would you pay for something that solved this completely?

I just published a guide on [connecting Python's logging module to OpenTelemetry](https://www.dash0.com/guides/opentelemetry-logging-python) without changing your existing logging calls. The `LoggingHandler` from the OpenTelemetry SDK plugs into `dictConfig` like any other handler, and it auto-attaches trace IDs when there's an active span. I also covered the alternative approach of skipping the handler entirely and letting the Collector ingest and process your JSON logs, which might be worth

Should i buy the course in 2026 . I already know python basics till oops . I saw the course structure from outside it looked good. Is it still revelent? Please drop your reviews and guide me

EDIT: working link [https://huggingface.co/bytedance-research/Lance](https://huggingface.co/bytedance-research/Lance) Lance is a lightweight native unified multimodal model that supports **image and video understanding, generation, and editing** within a single framework. * **Efficient at 3B scale.** With only **3B active parameters**, Lance delivers strong performance across image generation, image editing, and video generation benchmarks. * **Trained from scratch.** Lance is built with a sta

Upcoming Intel Xe3P data center GPU with 20 8GBLPDDR5X modules for a total of 160GB, bypassing HBM shortages. Assuming a 32-bit interface, that's a 640-bit wide memory interface, or 10 channel memory interface if converted to the 64-bit wide desktop equivalent. At 8800-9500MT, that's a 704-760GB/s memory bandwidth.

The video shows how my pipeline produces a 3D washing machine, composed of separated, functional parts (instead of monolithic 3D blobs). There's also hinge/socket articulation, so the internal assembly actually churns or rotates. The problem I'm solving: pretty much every text-to-3D pipeline right now is just diffusion (or diffusion-like) weights producing mesh blobs. Want to swap the scope on a generated gun? Change one word in your prompt, the whole or most of the thing regenerates from scrat

Agent decided to test if harmful command block worked by issuing a rm -rf / Thankfully it worked so only damage was a mild heart attack. I implemented a sandbox immediately afterwards. EDIT: for those wondering, I was implementing a bash command whitelist and also bubblewrap for isolation. I did the whitelist implementation first and that was the command the agent chose to test it 😂 bwrap got done quickly afterwards!

I’m upgrading from 32 to 48 soon and am excited but I’m curious what y’all run!

https://preview.redd.it/rajj11v7j42h1.png?width=1744&format=png&auto=webp&s=72381de22a9bac4b30a59498d549bb09df075df3 Hey, it's loubna from Hugging Face. Very happy to share our latest release: Carbon 🧬, a family of open DNA foundation models. Carbon-3B matches the current SOTA (Evo2-7B) while being 275x faster. We borrowed a lot from how modern LLMs are trained and from our SmolLM work, but DNA isn't language. Genomes are noisy, redundant, and shaped by evolution rather than com

Greetings from former TurboQuant's biggest defender, now middle-sized niche-aware TurboQuant defender. Today I'm presenting to you the results of me thoroughly exploring the world of PPL and KLD benchmarks with my single RTX 3090 using [BeeLlama v0.1.2](https://github.com/Anbeeld/beellama.cpp), with some backstory of unsuccessfully trying other tests and then re-exploring PPL and KLD even more thoroughly to compensate. Tests were done with Qwen 3.6 27B (`Q5_K_S` and `IQ4_XS`) at 64k and 128k co

Saw this video by them. Seems interesting but Tbh the benchmarks seem too good to be true. I'm not super knowledgeable on how models think so can anyone more knowledgeable explain what exactly is happening. And it's pros and cons? GitHub: https: //github.com/sapientinc/HRM-Text Hugging face: https://huggingface.co/sapientinc/HRM-Text-1B I'm not affiliated with them in anyway, just saw the video on YouTube.

[https://github.com/ggml-org/llama.cpp/pull/23269](https://github.com/ggml-org/llama.cpp/pull/23269)

Model Overview Nemotron-Labs-Diffusion is a tri-mode language model that supports both AR decoding and diffusion-based parallel decoding by simply switching the attention pattern of the same model during inference. The synergy between these two modes enables a third mode, called self-speculation: the same model performs diffusion-based parallel drafting and AR verification with shared KV cache, achieving high acceptance lengths and decoding efficiency. The seamless mode switching by simply chan

This is really just a post for those with shallow understanding of all this stuff, those not yet ready or capable of diving into the deeper end of vibe coding/llms. It might not be a helpful post for anyone more advanced than that. I have been working on a Python Pygame project for about two months. It is now sitting at roughly 30k lines of code across 55 modules. I have been using Visual Studio Code, Copilot Pro+, and around three times the cost of pro+ in additional premium requests per

My website is currently down, sorry about that. Here is my current full AI homelab setup: \#0 i3 7100, 32gb ddr4, 2x8tb archive nas (Archiving models here. Can serve them via samba nas if needed without needing to download them again.) \#1 ryzen 5600, 64gb ddr4, gtx1070 (Privateer, works surprisingly fast at running 35b a3b) \#2 ryzen 5950x, 128gb ddr4, rtx5060ti, strix x570f, Asus TUF gt502 (Manowar, can expand to 2x5060ti, without problems. tried with 2x3090, it overheats.) \#

For those of you running Qwen3.6:27B on 16GB VRAM, what quantization did you settle on? For my primary purpose as a HA voice assistant, I've found my ideal target to be >50 tg and >800 pp. Qwen3.5:9B works really fast, but I'm experimenting with higher intelligence. Offloaded the vision model to CPU because it is infrequently used. Currently running Qwen3.6-27B-Q3\_K\_S.gguf with 64 layers on GPU at the following speeds: prompt eval time = 462.66 ms / 507 tokens ( 0.91 ms

One way I like to test new models, is by one-shoting (with a good prompt) a single webpage clone of the classic arcade game pacman. I usually do 3 attempts and keep the best one. So far all of them, including anthropic, chatgpt and google models, have failed, most of them miserably. The best one until now was GLM 5.1 That was until I tried it with **Qwen 3.6 27b F16**. Out of 3 attempts, 2 were the best by far, with the top result only having minor errors! However, as soon as I dropped to 8bit

I'm currently running LM-Studio 0.4.12. In the app if I check for updates it says there's a new version (0.4.13), I can read the changelog for 0.4.13, but when I go to [https://lmstudio.ai/download](https://lmstudio.ai/download) it shows 0.4.7. What's going on here? Anyone knows?

Hey r/DeepSeek, Who says we need an H100 cluster or the latest expensive GPUs to run frontier MoE models? I wanted to see how far we could push a single node of consumer legacy hardware, so we spent less than $2,500 total to build a budget machine that successfully runs **DeepSeek-V4-Flash** (284B total, 13B active) locally! Surprisingly, we managed to hit around **255 prefill tokens/s** with a very tight memory budget. https://preview.redd.it/cfefgc71732h1.png?width=1772&format=png&a

Disclosure: I made this. Open-source, MIT, Windows + Linux. Not affiliated with [voiceflow.com](http://voiceflow.com) (the chatbot SaaS, name collision, sorry). Why this exists: I wanted local-only dictation and meeting transcription, because audio shouldn't have to leave the machine just to become text. I had a 6GB GPU sitting there doing nothing most of the day. So I built it: hold a hotkey, faster-whisper transcribes locally, text pastes at the cursor. v1.6.0 shipped today and adds the meeti

There's an increasingly popular way to ship complex extensions for agentic work, that is specific to Claude Code, which is Code plugins. For example here's [deep-wiki](https://github.com/microsoft/skills/tree/main/.github/plugins/deep-wiki) by Microsoft, a plugin to create a wiki from analyzing your project's repo. There's a lot in there. It's far more powerful than a skill, it can let the user do manual activation via slash commands (/deep-wiki:generate, /deek-wiki:ask, etc), it can spawns dif

I first posted about [PrivateScribe.ai](http://PrivateScribe.ai) \~1yr ago and have recently jumped back intent on bringing it to a functionality that makes it actually usable by non-technical users. One year ago it worked but only the bare minimum. Since then I've gotten ⭐️74 github stars!⭐️ and have had a few meetings with people that has inspired me to push it forward. PrivateScribe is a fully local, open source AI transcription platform using FasterWhisper, pyannote, and Ollama, built with

Title. I've got a llama.cpp server running a model being accessed across a number of scripts, and some of them are easier for the model than others, and those easier ones are also latency dependent. Rather than host two different servers with different parameters, I'd rather just send something along with the prompt to disable it. If I must host multiple servers, am I able to host two servers for the same model but only have the model loaded in memory once? VRAM limited, like most of you I'm su

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