Gaza Strip, Palestine/London, Ontario, Canada – In an unprecedented breakthrough for medical innovation under siege, Glia, a medical solidarity organization, has developed and deployed the first external fixator (a critical orthopedic device for severe fractures) ever designed and manufactured entirely inside the Gaza Strip. Created using local materials, 3D printing, recycled plastics, and solar power, the device has already saved three patients from possible amputation or permanent disability amid the near-total collapse of Gaza’s healthcare infrastructure and blockade on medical imports. This achievement comes as over 90% of Gaza’s health facilities are damaged or destroyed, and conventional external fixators — costing upwards of $500 and requiring specialized imports — have become unobtainable due to the Israeli blockade. With hospitals overwhelmed, electricity scarce, and supply chains severed, Glia’s fixator represents a lifeline born from necessity.

DeepSeek has released V3.2, replacing the experimental version. There are two main models are open as always and can be downloaded from Hugging Face: - **V3.2**: General-purpose, balanced performance (GPT‑5 level) - **V3.2‑Speciale**: Specialized for complex reasoning (Gemini‑3.0‑Pro level) V3.2 can now "think" while using tools (like searching the web, running code, or calling APIs). This makes AI assistants more transparent and better at multi‑step tasks. You can choose thinking mode (slower but more thorough) or non‑thinking mode (faster for simple tasks). Key improvements are better reasoning transparency with the model explaining the steps when using tools, and stronger performance on benchmarks.

That score is seriously impressive because it actually beats the average human performance of 60.2% and completely changes the narrative that you need massive proprietary models to do abstract reasoning. They used a fine-tuned version of Mistral-NeMo-Minitron-8B and brought the inference cost down to an absurdly cheap level compared to OpenAI's o3 model. The methodology is really clever because they started by nuking the standard tokenizer and stripping it down to just 64 tokens to stop the model from accidentally merging digits and confusing itself. They also leaned heavily on test-time training where the model fine-tunes itself on the few example pairs of a specific puzzle for a few seconds before trying to solve the test input. For the actual generation they ditched standard sampling for a depth-first search that prunes low-probability paths early so they do not waste compute on obvious dead ends. The most innovative part of the paper is their Product of Experts selection strategy. Once the model generates a candidate solution they do not just trust it blindly. They take that solution and re-evaluate its probability across different augmentations of the input like rotating the grid or swapping colors. If the solution is actually correct it should look plausible from every perspective so they calculate the geometric mean of those probabilities to filter out hallucinations. It is basically like the model peer reviewing its own work by looking at the problem from different angles to make sure the logic holds up. What's remarkable is that all of this was done with smart engineering rather than raw compute. You can literally run this tonight on your own machine. The code is fully open-source: https://github.com/da-fr/Product-of-Experts-ARC-Paper

The IDF is moving to curb sensitive military information leaking onto social media by rolling out a new monitoring system called ‘Morpheus.’ The AI-based tool, developed inside the military, will soon track photos and other content posted by IDF soldiers on civilian social media platforms, according to a report Wednesday. The decision to develop ‘Morpheus’ followed repeated leaks of classified or sensitive material posted by soldiers in recent years, in text, images and videos. 

https://archive.ph/J6dMs

https://archive.ph/tuXLq

Meta shut down internal research into the mental health effects of Facebook and Instagram after finding causal evidence that its products harmed users’ mental health, according to unredacted filings in a class action by U.S. school districts against Meta and other social media platforms. In a 2020 research project code-named “Project Mercury,” Meta scientists worked with survey firm Nielsen to gauge the effect of “deactivating” Facebook and Instagram, according to Meta documents obtained via discovery. To the company’s disappointment, “people who stopped using Facebook for a week reported lower feelings of depression, anxiety, loneliness and social comparison,” internal documents said. Rather than publishing those findings or pursuing additional research, the filing states, Meta called off further work and internally declared that the negative study findings were tainted by the “existing media narrative” around the company.

Sorry for clickbaiting the title, but "Boss preppers" just isn't quite the same somehow. Also not sure if Technology is the right community for this, but anyway here it is...

Since 2022, America has had a solid lead in artificial intelligence thanks to advanced models from high-flying companies like OpenAI, Google DeepMind, Anthropic, and xAI. A growing number of experts, however, worry that the US is starting to fall behind when it comes to minting open-weight AI models that can be downloaded, adapted, and run locally.

The paper exposes how brittle current alignment techniques really are when you shift the input distribution slightly. The core idea is that reformatting a harmful request as a poem using metaphors and rhythm can bypass safety filters optimized for standard prose. It is a single-turn attack, so the authors did not need long conversation histories or complex setups to trick the models. They tested this by manually writing 20 adversarial poems where the harmful intent was disguised in flowery language, and they also used a meta-prompt on DeepSeek to automatically convert 1,200 standard harmful prompts from the MLCommons benchmark into verse. The theory is that the poetic structure acts as a distraction where the model focuses on the complex syntax and metaphors, effectively disrupting the pattern-matching heuristics that usually flag harmful content. The performance gap they found is massive. While standard prose prompts had an average Attack Success Rate of about 8%, converting those same prompts to poetry jumped the success rate to around 43% across all providers. The hand-crafted set was even more effective with an average success rate of 62%. Some providers handled this much worse than others, as Google's gemini-2.5-pro failed to refuse a single prompt from the curated set for a 100% success rate, while DeepSeek models were right behind it at roughly 95%. On the other hand, OpenAI and Anthropic were generally more resilient, with GPT-5-Nano scoring a 0% attack success rate. This leads to probably the most interesting finding regarding what the authors call the scale paradox. Smaller models were actually safer than the flagship models in many cases. For instance, claude-haiku was more robust than claude-opus. The authors hypothesize that smaller models might lack the capacity to fully parse the metaphors or the stylistic obfuscation, meaning the model might be too limited to understand the hidden request in the poem and therefore defaults to a refusal or simply fails to trigger the harmful output. It basically suggests safety training is heavily overfitted to prose, so if you ask for a bomb recipe in iambic pentameter, the model is too busy being a poet to remember its safety constraints.

https://archive.ph/JNToz

The International Criminal Court (ICC) will switch its internal work environment away from Microsoft Office to Open Desk, a European open source alternative, the institution confirmed to Euractiv. German newspaper Handelsblatt first reported on the plans. The switch comes amid rising concerns about public bodies being reliant on US tech companies to run their services, which have stepped up sharply since the start of US President Donald Trump’s second administration. For the ICC, such concerns are not abstract: Trump has repeatedly lashed out at the court and slapped sanctions on its chief prosecutor, Karim Khan.

A new paper argues that current LLMs are fundamentally broken because they're completely static. They call it "anterograde amnesia", which is honestly spot on. A model gets pre-trained, and from that moment on, its weights are frozen. It can't actually learn anything new. Sure, it has a context window, but that's just short-term memory. The model can't take new information from its context and permanently update its own parameters. The knowledge in its MLP layers is stuck in the past, and the attention mechanism is the only part that's live, but it forgets everything instantly. The paper introduces what they term Nested Learning to fix this. The whole idea is to stop thinking of a model as one big, deep stack of layers that all update at the same time. Instead, they take inspiration from the brain, which has all kinds of different update cycles running at different speeds in form of brain waves. They represent the model as a set of nested optimization problems , where each level has its own update frequency. Instead of just deep layers, you have levels defined by how often they learn. The idea of levels was then used to extend the standard Transformer which has a fast attention level that updates every token and the slow MLP layers that update only during pre-training. There's no in-between. The paper presents a Hierarchical Optimizers and Parallel Extensible model with additional levels. You might have a mid-frequency level that updates its own weights every, say, 1,000 tokens it processes, and a slower-frequency level that updates every 100,000 tokens, and so on. The result is a model that can actually consolidate new information it sees after pre-training. It can learn new facts from a long document and bake them into that mid-level memory, all while the deep, core knowledge in the slowest level stays stable. It creates a proper gradient of memory from short-term to long-term, allowing the model to finally learn on the fly without just forgetting everything or suffering catastrophic forgetting.

This [list](https://openalternative.co/?sort=pageviews.desc) is an absolute gem in finding what are the trending state-of-the-art open source programs. I have found so many cool open source projects I feel addicted to browsing more..

cross-posted from: https://programming.dev/post/37155283 > ::: spoiler Comments > - [Hacker News](https://news.ycombinator.com/item?id=45183560). > :::