...antibody therapeutics with more effective mechanisms than before. Such mechanisms are urgently needed in food allergy, which involve overcoming one of the tightest affinity protein pairs in immunology, IgE and its receptor FcεRI, on mast cells.   Treatment with Xolair—the only approved antibody for food allergy—sequesters circulating IgE, but mast cells remain sensitized for weeks to months due to the extraordinarily slow dissociation of IgE from FcεRI.   We envision a treatment that actively pulls IgE off of FcεRI, without waiting for natural dissociation. Like picking a lock, this requires the right combination of geometry and force.   Our research team, working with Claude, ideated on mechanisms to accomplish this disruption, and converged on an antibody format with two arms with different functions and epitopes. Within one month, we designed, synthesized, and experimentally confirmed the biophysical function of the two arms, which we are now assembling into full therapeutic formats.     Read more:   Anthropic’s blog   https://   anthropic.com/research/early   -progress-drug-development   …   Our technical report   https://   divergencelabs.com/publications/i   ge-preprint.pdf   …     — Bowen Jing

Source: https://x.com/bjing2016/status/2115235758802161846

I'm building an entire content site using affiliate links and eventually google display ads to generate income. It's blowing my mind how powerful/capable Claude opus 5.5 is, just on medium, to do all of this on its own (I'm on the $100 a month plan). I'll know in ~6 months from now if this idea was viable or not, but what about you guys? Any business savvy people in this sub using the capabilities of AI to generate cash money? 💰🤑💸

So I had this issue where the game facial mood animations we're freezing my lipsync. Finally fixed now and now it uses the game facial animations + my facial animations. Also voice audio is now passed trough the engine itself so it can take advantage of all the game effects like reverb, occlusion or even phone filter.

With OpenAI recently sharing its Navier-Stokes solution, hundreds of mathematical results on GitHub, and now new research in mathematics and physics, it is pretty exciting to see how quickly AI is progressing in these fields.

I have been thinking a lot about how people could use their own AI agents to work on mathematical problems in a more structured way.

So I built MathTrace, a lightweight skill based mathematical research workflow with a Python runtime. It allows agents to explore possible solutions, question assumptions, search for counterexamples and review each others reasoning. It also keeps track of mathematical claims and evidence, with Lean 4 integration for formal proof checking.

I spent quite a bit of time thinking about how the different parts should work together. The idea is not to build another AI model, but to make it easier for people to use the coding agents they already have for mathematical research.

I'm curious what others think about this approach, especially with how fast AI mathematics is progressing.

https://github.com/Velor-dev/MathTrace

Spend lot of time to prompting and adjusting until I feel it's good enought to show you guys.

We’ve been using our OSS coding harness to do pen testing on our software overnight. While we’re not there yet, we’re planning on experimenting with auto remediation as well. We’ve found the tool to be extremely useful, especially when performing adversarial reviews (look at some of our recently opened PRs for an idea of code quality). I’d be happy to support anyone who wants to give it a try on their project.

No miracle [yet], but really intriguing hints. https://www.tomsguide.com/ai/openai-unleashed-thousands-of-ai-agents-to-study-a-parkinson-like-disease-heres-what-they-found-in-32-minutes

"The researchers used GPT-Rosalind, OpenAI's specialized AI model for life sciences, to examine a dataset showing what happens when different genes are disrupted in mouse brain cells.

Each agent focused on a specific genetic change, studied how cells responded and searched scientific literature for possible explanations. The findings were then combined into what the researchers call a "reasoning atlas," connecting genes, cellular changes and existing scientific knowledge.

The team estimates that reviewing the same material manually, one genetic change at a time, could take months to a year. The AI agents completed their analysis in just 32 minutes."

I am a senior engineer at my company. I don't do a damn thing. I sit at my desk all day and answer emails because I'm the SME (subject matter expert) lead on many various topics, programs, projects, regulations, etc.

I used to do this job okay. A question/project would pop up and I'd do a little digging, research some documents, do a little math and/or science, draft a document, revise some papers. But now?

Plug that into an LLM. Everything. It gives me what I ask for, how to do it, the math, the science, the regulations, the whole damn thing. Ofc it ain't perfect...but it's damn near. I find that 90% of the time it does my job perfectly without me having to raise a finger. The other 10% is a bit re-wording and minor edits. I also use it as a mentor to bounce ideas off of and confirm my decisions.

Nobody at the company knows I do this. It's scary how accurate AI is nowadays. I feel like you could pay somebody minimum wage to just prompt AI like I do.

Heck, it even knows stuff I don't. Today I got an email from a stakeholder that stumped me. Had zero clue how to answer his question. Put it into AI and AI told me everything...from what to say, the evidence (show me your work), and the whole damn 9 yards. It even provided me with links to provide to them so as to strengthen my argument/credibility/advice.

The ONLY thing is AI is so damn robotic it's obvious you're using it. So that's where I just come in and rewrite it to sound...more DUMB and more like a regular human.

I'm scared, guys. Senior IC (individual contributor) roles are going to be slowly vanishing. But fuck it, enjoying this while it lasts.

EDIT: Oh, still gotta do pointless meetings, though. There's still that which AI cannot do. So until that gets solved anytime soon...I guess my job's still somewhat secured.

EDIT2: Also the most annoying thing is AI websites are ALL blocked on my work laptop. So in order for me to do my AI shenanigans, I gotta take a picture of the email/project/whatever via my phone and then just type in what my AI replies back to me on that. A bit time consuming but it's worth it.

Hello! I've been playing a board game called Go (Baduk/Weiqi) for about five years, and over that time I've done quite a few experiments with AI and neural networks.

One problem I've been particularly interested in is AI explainability. Modern Go engines can play at a superhuman level, but understanding why they make certain decisions is much harder.

Some existing tools attempt to solve this by feeding AI moves into an LLM and asking it to explain why those moves are good. The issue is that LLMs can hallucinate explanations that sound convincing but don't necessarily make sense in the actual position.

I decided to experiment with the opposite approach.

Rather than having an AI make a decision and trying to explain it afterward, I built a system that generates possible moves from a predefined dictionary of Go shapes, patterns, strategies, and proverbs. Each pattern has an explanation associated with it.

These explanations were developed using LLMs alongside feedback from experienced Go players. Rather than relying on an LLM to generate an explanation every time a move is made, the explanations are prepared in advance and tied to specific patterns and heuristics.

I then use KataGo, an existing neural-network-based Go engine, to evaluate these candidate moves and select the strongest one.

This means the AI is restricted to moves that can be associated with recognizable Go concepts. It sacrifices some playing strength in exchange for making its recommendations easier to understand.

What's been interesting is that the system still performs at approximately 1 dan in my testing, which is a reasonably strong amateur level.

I've integrated this into a Go application I've been developing called Companion Baduk, where it powers the game review system. Players can step through their games and explore recommended moves alongside explanations of the shapes and strategies involved.

The eventual goal is to integrate an LLM more directly into this system, allowing players to ask questions about specific positions, moves, and strategies. Rather than having the LLM independently interpret the board and potentially hallucinate explanations, it would use the existing heuristic explanations and neural-network evaluations as a foundation for its responses.

I've already been experimenting with this, but it's still a work in progress. One of the main challenges has been maintaining enough context for the LLM to understand the board position, relevant patterns, and previous discussion. The available context gets used up surprisingly quickly, especially when trying to support ongoing conversations about a game.

I wouldn't claim this makes the underlying neural network itself interpretable, but it does provide a way to combine neural-network evaluation with human-understandable heuristics. The hope is that eventually an LLM could use this structured information to provide more natural, interactive instruction while reducing the risk of fabricated explanations.

I think there's a lot of potential in this kind of hybrid approach, especially for educational AI systems where understanding a decision can be just as important as getting the optimal result.

I'm curious what people think about this approach to explainable AI, and whether similar ideas could be useful outside of board games.

For anyone interested, here's the application I've been developing:

https://store.steampowered.com/app/4698830/Companion_Baduk/

I’ve been making games for 19 years. Over the last few years it had turned into a job, and I stopped enjoying it. A few weeks ago I set myself a challenge, take one small game through the entire pipeline on my own with AI. That meant development, art, marketing, the Steam page, and getting it onto the platform.

It took about 5 weeks. For the first time in years I was actually having fun making a game again.

In the game, you drive a machine into a dense field, clear it, and use what you earned to buy the next field. It's a chill, unrealistic farming game. A couple of gamedev subs removed my posts as I disclosed AI use, so I’m sharing the process here instead, for anyone curious how it actually works.

The stack

• Claude (Opus 5.5) in Claude Code wrote basically all of the C#. I worked with it like a small team of agents.
• Funplay MCP + Unity 6 let Claude drive the Unity editor directly. It can inspect scenes, change values, take screenshots and play the game, so it can see what it built instead of guessing.
  • Having tested several MCPs: Funplay is the best one IMO
• Magnific for concept art, textures and material maps.
• Tripo to turn picked concepts into 3D models.
• Blender + MCP, driven by scripts, to clean up, split, rescale and fix those models before they go into the game.

The automated part
This is the part I find most interesting. I ended up with a backlog of cards by writing out plans, design ideas and feedback. A runner script takes one card at a time while I’m away (like sleeping), with a hard time and money budget per card. It works in its own branch, builds the game headlessly, runs tests, drives the built game to measure handling, takes screenshots, and only lands the change if everything passes.

The marketing side runs the same way. Scripted camera takes are automatically filmed inside the game, cut into shorts, and queued on a posting schedule.

I still made every taste call: how it feels, how it looks, what’s fun. AI didn’t decide what the game was. It still took a lot of manual game design work and feedback to iterate on cutting mechanics and crops, camera, tractor structure and it's movement, and level/environment setup.

If anyone wants details on any part of this, ask away. I’m happy to share what worked and what didn’t.

And if you’re curious about the game itself, here it is: https://store.steampowered.com/app/5341880

"“The answer is not to try to prohibit the activity; any effort to do so is just as unlikely to succeed as the technology and its enablers are likely to push the boundaries of the law,” the op-ed said. “The answer is to wrap AI in legal form. Give an autonomous system a recognizable legal identity and you make it legible to law. You create a defined target to which responsibility and damages can attach. You make the agent’s conduct visible, traceable, and accountable.”"