As a European I have long given up on any meaningful change w.r.t AI. Imho the average European is much more risk averse than the average American or Chinese. That and a plethora of other factors that have been discussed over and over again, make it unlikely that we'll see things change within the next ten years or so. Only massive and immediate threats (e.g. he crisis in Ukraine) will make people and governments reconsider their fundamental beliefs (and even then the pace of change will be slow).
Awesome work! If I understand it correctly, it loads relevant subgraphs from the DB and then runs queries in Prolog on it? Or is it more similar to datalog?
I've worked extensively with the slightly less able cousin, the 35B A3B model and tuned my own harness around making it work well with local or non-sota models. The results are quite promising [0], if one sticks to a plan-execute approach. After a bit of fiddling with llama.cpp I was able to get it to work through a small change on a real codebase from work on a 32GB M5 (typical python FastAPI backend, so nothing out of the ordinary). While that's somewhat encouraging, the whole local experience was still far from pleasant with all the noise and heat.
Mathematics has always been an experimental science to some extent. While Newton, Euler and Gauss would spend a lot time calculating numerical approximations by hand, modern mathematicians have been doing the same using computers and software. And once an a clear picture emerge about what’s going, you can start to formalize that and attempt to prove and communicate your results in the standard definition, proposition, lemma,
theorem scheme. (Btw there is even a journal called Experimental Mathematics devoted to this approach).
I don’t see that LLMs will fundamentally change this,
but rather accelerate the speed
of mathematical research.
Some computer generated proofs might of course be hard to understand, but at least their existence gives another data point work with.
Doing Mathematics is more than proving something, that’s just the end of a long road spent pondering at one’s desk about how things could work out.
This somehow resonates with me and I feel this is one of the negative side effects of a CS/Maths dominated culture and mindset that strongly emphasizes intellectual achievement, but hasn’t yet matured enough to appreciate the more messy and irrational parts of our existence.
I’ve been working on my own harness / “orchestration layer”, not with the goal of reaching frontier level performance, but rather boosting performance of smaller (locally hostable) models. Unfortunately, I don’t have VC money to burn on running hundreds of evals, but some preliminary results do indicate that it could work[0].
What worries me personally is the dopamine hit I seem to get from watching my ideas get built in front of me. There is a big temptation to just add feature after feature without really checking what the code actually looks like. So yes, more discipline is needed.
I’ve been playing around with qwen3.6-35b-a3b and managed to boost it significantly by leveraging my own custom harness [0].
It is quite astonishing to see how far local models have progressed, and I think that if you enjoy tinkering a bit, you can save a good bit of money (if you happen to have the hardware lying around anyways). Overall it’s still hard to beat the the cost/convenience combination of a cloud based model provider though.
I’ve been building a Prolog and WASM based LLM/Agent framework [0] , where reusable skills and core harness functionality is encoded in a logic programming language. Recently added a small TUI with a Borland Turbo Vision style design. My goal is to build a completely hackable harness that works well with smaller models and to further promote the combination of logic programming and LLMs.
I've been working on DML, a Prolog-based DSL [0] used to define and orchestrate agents and LLM workflows. It's been quite fun, although - given the amazing capabilities of SOTA models - I am not so sure anymore how meaningful it will be to continue with this work. Anyways, the language and also supports DCGs, so it should allow for plenty of interesting ways to combine grammars, LLMs, agents etc.
As someone who has developed a somewhat weird obsession with Prolog, I can highly recommend Markus Triska's other articles on Prolog. His article on meta-interpreters [0] was particularly inspiring for me.
The idea is to take markdown instructions and "compile" them into a Prolog-based DSL that orchestrates both deterministic and LLM-based components. The (meta-)interpreter of the DSL automatically tracks the entire execution process, so that the final ouput becomes observable and more explainable. Still at an early stage, but I am having lots of fun with it and would love to explore possible use cases.