This week in tech: 23.02.2026
Summary of AI developments - made for busy people
APPLICATIONS
OpenMed has released a large open-source suite of multilingual healthcare PII detection models:
105 models for French, German, and Italian trained on tens of thousands of native-language samples
Each model covering 55+ PII entity types and achieving 97pct accuracy
Built for real healthcare workflows with language-specific identifiers (e.g., NSS, Sozialversicherungsnummer, Codice Fiscale)
Sizes between 33 and 600M parameters.
Designed for compliant, production use - HIPAA/GDPR-aligned, on-prem-friendly ;-)
Apache 2.0 license
HF model page: https://huggingface.co/collections/OpenMed/multilingual-pii-and-de-identification
Generative AI + robotics = ? Boston Dynamics has integrated Google Gemini into the Atlas robot to provide “advanced reasoning” and “adaptive decision-making” for real world environments. The idea is to enable the signature BD humanoids to perform industrial tasks and autonomous maneuvers with (greater) independence.
https://bostondynamics.com/blog/boston-dynamics-google-deepmind-form-new-ai-partnership/
Ok, this is getting asinine: first a bot-native Reddit, now Github?
Ex-Github CEO is launching “Entire” - a platform designed to treat AI agent interactions as a core part of the version control process.
First tool is Checkpoints: automatically captures the prompts, reasoning, and metadata behind AI-generated code. If you strip away the marketing, it’s actually not a bad way to validate - make the “intent” behind every change searchable and traceable.
All AI session data is stored on a separate branch, allowing developers to audit agent logic without polluting the main commit history.
Entire is built to work with tools like Claude Code and Gemini CLI, it aims to coordinate multi-agent workflows within a Git-compatible database.
Repo: https://github.com/entireio/cli
Frontex is an EU agency that “supports EU Member States and Schengen-associated countries in the management of the EU's external borders and the fight against cross-border crime” - judging by the state of affairs around the refugee crisis, it’s hard not to conclude that they not very good at their jobs… But maybe it will change with AI? At a modest (for EU standards) cost of 0.5mln UER, they are developing a mobile application to assist individuals undergoing voluntary or forced deportation from Europe. The app features an AI chatbot that uses an LLM to provide information on financial incentives and local counseling centers.
The project aims to streamline the return process by feeding specific datasets into the bot to help deportees navigate available services in their countries of citizenship. I hope the authors are good with traffic at scale, because I’m sure it will be massively popular and all the illegals will self-deport.
Another day, another raging success story - in the making.
https://algorithmwatch.org/en/frontex-is-building-an-ai-chatbot-app-to-encourage-repatriations/
MOSS-TTS family of models is here:
Open-source speech synthesis, Apache 2.0 license
Five production-ready models
The entire family is powered by the MOSS-Audio-Tokenizer: 1.6B parameter, CNN-free model, trained on 3 million hours of audio
The flagship MOSS-TTSD model supports up to 16 minutes of coherent, zero-shot voice cloning for up to 5 speakers in a single inference call
MOSS-VoiceGenerator does not require reference audio, it allows users to “design” unique speaker embeddings using simple text descriptions like “a warm female voice with a slight British accent.”
Full spectrum of audio needs: low-latency 1.7B model for real-time agents, an 8B flagship for zero-shot cloning, and a specialized model for environmental sound effect generation.
Repo: https://github.com/OpenMOSS/MOSS-TTS
HF model page: https://huggingface.co/collections/OpenMOSS-Team/moss-tts
Repeating your entire prompt twice, i.e. literally doing [PROMPT][PROMPT] boosts LLM performance by allowing the model to re-process context with the final question already in “view” - which overcomes the structural limitations of left-to-right token processing.
This has got to be the most low-effort, zero-cost hack in the history of hacks :-) Disclaimer: it improves non-reasoning models, is redundant for reasoning ones - those rephrase prompts during their internal chain of thought.
Paper: https://arxiv.org/abs/2512.14982
BUSINESS
Grab the loot (1):
OpenAI has formally accused the DeepSeek of “free-riding” by using (programmatic) distillation to harvest knowledge from its models to train competitors like DeepSeek-R1. Altman and his minions allege that their Chinese counterparts bypassed access controls using obfuscated routers and other terrible, horrible, no good at all practices.
Speaking of privacy: across the pond a federal judge ruled that documents and chat transcripts generated via AI are not protected by attorney-client privilege - even if they were created to prepare for legal counsel. This means that sharing information with ChatGPT and whatnot constitutes a waiver of confidentiality, and it turns your prompts into discoverable evidence.
https://mashable.com/article/ai-attorney-client-privilege-court-evidence
CUTTING EDGE
MiniMax-2.5 has landed:
Built for local execution and efficiency for agentic and coding tasks.
230B total parameters, sparse Mixture-of-Experts architecture with 10B active parameters - beating DeepSeek V3 and GLM-5 on benchmarks
Optimized for local use, it can run at 20 tokens/s on a 128GB Mac using dynamic precision or at 8-bit on 256GB setups
80pct score on SWE-Bench Verified, way cheaper than Opus 4.6 => more bang for your buck
Announcement: https://www.minimax.io/news/minimax-m25
Run it locally - guide: https://unsloth.ai/docs/models/minimax-2.5
HF model page: https://huggingface.co/MiniMaxAI/MiniMax-M2.5
Deep dive:
President of AI can into education: Andrej Karpathy has released a dependency-free, 200-line Python script that strips GPT training down to its raw essentials (no high-level tensor libraries or GPU acceleration). The code features a custom scalar autograd that manually calculates gradients for basic operations, exposing the complete transformer algorithm for direct study. By running as a single file on a CPU, you can adjust model parameters and trace every gradient path through the entire training / inference cycle.
Try it yourself: HERE.
Alibaba’s Qwen-Image-2.0 smashed into the top three on the AI Arena leaderboard:
SOTA high-fidelity image generation and precise text rendering.
7B parameters - smaller than its predecessor - built on Flow Matching architecture
Outperforms much larger systems in generating pixel-perfect text, intricate calligraphy, and professional PowerPoint layouts.
Project: https://chat.qwen.ai/?inputFeature=t2i
Blog: https://qwen.ai/blog?id=qwen-image-2.0
Say hello to GLM-5:
New launch from Z.ai has 744B params AND open-weights (if you have the hardware to run it)
Pitched as a rival to flagship closed systems in engineering and agentic tasks.
Mixture-of-Experts design, 40B active
Optimized for “Long-Horizon Engineering”, beats many proprietary models in multi-step planning and business simulations.
Trained entirely on Huawei Ascend hardware - guess the sanctions didn’t work after all
MIT license
Announcement (links therein): https://z.ai/blog/glm-5
Remember OpenClaw? The model responsible for unleashing a mania, forming a social network for bots - and spiking Mac Mini sales, so everyone could run their own instance in YOLO mode? Seems like the last one is not the case anymore - I mean, the yolo part holds, but you can do it way cheaper: a bunch of absolute madlads recoded the whole thing in Go. Result? You can now unleash your own private terminator for 10 USD, because it runs on Raspberry Pi.
No, it’s not a typo: the Chinese engineers rebuilt the whole thing, it works for a fraction of the original cost - and it’s faster. Respect.
Repo: https://github.com/sipeed/picoclaw
FRINGE
You seriously cannot make it up:
OpenAI is planning to launch
sex chatbotsadult mode in ChatGPT - which is pretty much guaranteed to turn part of its base into gooning moronsA female VP wants to stop it - objectively a good thing
She gets accused of sexually harassing a male employee and fired
The Online Safety Act in the UK is a harbinger of things to come: a nightmarish legislation that looks like something out a hangover nightmare Orwell and Kafka had after a night out drinking. Its official intention - to protect the children from harm - would’ve been ever-so-slightly more believable if it hadn’t been for the fact that one of its architects just pleaded guilty to child sex offenses. He was endorsed by the British PM - who, funny enough, deleted the tweets. Between this and the association with Peter Mandelson, Keir Starmer is not the man without qualities I thought he was.
Good thing the condition is treatable.
If you feel like peeking into the future, r/chatgpt is a good place to go: it’s absolute ground zero for AI-induced psychosis. When OpenAI retired the 4o-mini variant of ChatGPT, people were having meltdowns not seen this side of the 2024 US presidential election.
Guess what? We are about to get a sequel - and unlike Agent Orange presidency, this one is not ending any time soon.
https://futurism.com/artificial-intelligence/chatgpt-crashing-out-openai-retiring-gpt-4o
Not quite the AI apocalypse I was expecting - then again, in the beginning nobody took the Bolsheviks seriously either. We have the first official instance of a human contributor rejecting an open-source pull request because it was generated by an AI agent - and the bot generated an angry response (there were expletives involved, so I wonder which LLM was used ;-)
As usual with these things, a debate about “rights” erupted right away.
Github thread: https://github.com/matplotlib/matplotlib/pull/31132#issuecomment-3881491475
Grab the loot (2):
According to Google, somebody tried to clone Gemini by prompting it over 100 thousand times - using the standard API, no less. The idea that proprietary data (if that’s what we call scraping everything online) and the underlying intelligence are at risk? There is something deeply karmic about the idea.
RESEARCH
Robot learning is the next frontier - so obviously LLM are coming out to play. Autoregressive policies are powerful rely on action tokenization that is compact, decodable, and causally ordered - which is a challenge for the current paradigm, even in a (relatively) simpler setting like time series. This paper introduces Ordered Action Tokenization (OAT): a learned tokenizer that aligns continuous actions with next-token prediction and enables prefix-based control.
Paper: https://arxiv.org/abs/2602.04215
Garbage in, garbage out: who would’ve thought it was still true AD 2026? Turns out that if you train LLM on Reddit data, you replicate the politics (and the average IQ) of Reddit. To wit: major AI models show systemic decision biases when it comes to hiring, loan, or admission tasks
Gender bias favoring female applicants appeared in 5/6 models, and race/ethnicity bias favoring minority-associated names in 4/6 models
The analysis detected new influences not covered by earlier manual audits: religious affiliation in loan decisions, English proficiency and writing formality - which shows that models “infer” sensitive traits from indirect cues.
Models shifted outcomes without mentioning the sensitive attribute in their chain-of-thought
Yes, I know - AI Act, DSA, ban on profiling, blah blah blah… But if an LLM is merely used on the side by a person making a decision - and let’s be honest, at scale it will happen? Good luck detecting that.
Paper: https://arxiv.org/abs/2602.10117
Repo: https://github.com/FlyingPumba/biases-in-the-blind-spot
Meet Aletheia, a math research agent built on Gemini Deep Think that generates publishable papers. It has already proven its capabilities by solving open Erdős problems through both autonomous and collaborative efforts - although tbf, the originality of the proofs is under dispute.
Repo: https://github.com/google-deepmind/superhuman/tree/main/aletheia
