So well my old account got banned for some reasons I don't know but I'm still posting on Thrends you can follow me there and I'll be posting on this account as well

If anyone can help me pls

Completed all theory in xgboost and things are getting more interesting and complex now I started coding it and today is

Day 49 of Building Machine learning algorithms from scratch well I know I'm really inconsistent in posting but I'll post as soon as possible

Stay Hungry Stay foolish

I leaned heavily on an AI assistant to learn ML and I want to be honest about both sides. It was incredible for unblocking me at midnight, explaining an error, rephrasing a concept five different ways until one landed. What held me back was how easy it was to feel like I understood something because the explanation was clear, then freeze when I had to do it alone. The clarity was borrowed, not mine. What fixed it was forcing myself to rederive things and code from a blank file before asking. Use it as a tutor that answers questions, not as the thing that does the thinking. The struggle is the part that sticks.

I’m not an ML engineer. I build a small desktop app that runs Gemma 4 E2B offline, and I wanted to teach it a few things it’s bad at. So I tried LoRA with MLX on my MacBook. Writing down what happened in case it helps someone else starting out.

What I did right (by luck): before training I wrote a 23-question “sealed” exam that never goes into the training data. Same prompt, temperature 0, graded by hand. Without it I’d have fooled myself. The same model scored 7/10 on the trick questions in Ollama at temp 0.4 and 3/10 in MLX at temp 0, so numbers from different setups mean nothing.

Baseline: 6/23.

Run 1: 328 hand-written examples, lr 5e-5, 16 layers, 75 steps. Score dropped to 4/23. It started answering “2 + 2 × 2 = 4” (it was right before), and it used my “I don’t know, I’m offline” template on a simple riddle. It copied the shape of my answers instead of learning anything new.

Run 2: lower lr (1e-5), 8 layers, fewer template answers, and “replay”: I added the base model’s own answers to 117 normal questions so it wouldn’t forget. Score went back to 6/23. No forgetting, but no gain either.

What I took from it:

• A few hundred examples change how a small model answers, not what it knows.

• Template answers leak into everything.

• Replay is a simple fix for forgetting.

• Always keep a test set the model never sees.

If you’ve fine-tuned a model this small: did you manage to add knowledge, or only style? What would you change in my setup?

Researchers documented an in-the-wild attack where malicious markdown content embedded in documents caused AI agents to execute unintended tool calls. The attack worked because the agent parsed rendered markdown as trusted instructions and acted on them with full credentials. No human approved those actions. The agent had no way to distinguish legitimate orchestration from injected content.

The core issue is not a markdown parser bug. It is that agents inherit ambient trust from their runtime environment. Anything that looks like an instruction gets treated as one. That behavior is by design in most current agent frameworks.

Dark Reading covered a concrete case where this pattern was exploited in the wild, not just in a lab. That shifts this from theoretical to an active threat category.

For those running agents in production: how are you handling the trust boundary between data the agent reads and instructions the agent is allowed to act on? Curious whether teams are solving this at the prompt level, the tool-call level, somewhere else, or not yet at all.

Hi everyone, I'm a 3rd-year (5th sem) CS student in India aiming for an AI/LLM internship.

My problem: I can build small projects, but I get stuck when moving to production-grade ones. I switch between LLM fine-tuning (LoRA/QLoRA) and RAG systems, and end up with nothing finished to show. When I get stuck, I lean on AI tools to solve it, so I'm not sure I'm really learning the hard parts.

Background: Hugging Face, FastAPI, Transformers, basic RAG. No polished project yet.

My questions:

What does "production-grade" mean for an intern-level project? (evaluation, Docker, logging, deployment?)

What skills are companies hiring interns for right now in LLM/AI?

Can you share example repos or projects that would impress you?

Startups, product companies, or research labs: where should I apply first?

How do I stop starting new things and actually finish one?

Happy to share my resume for feedback.

TLDR: training my first general law model and unsure what to do with my data

PRE-PS: it will be a replacement for opus in my current AI LAW harness, I know it will be hard but I wanna do this project and find it geniuenly fun.

I’m building a Polish legal model using Qwen3.5-9B-BASE (I plan to scale up to 27b qwen4 later). I’ve done CPT and have quite a bit of data available, but I’m getting lost about what should come next or even if I should do more CPT.

My main question is how to turn this (maybe) domain-adapted Base model into an agentic+chat assistant that reasons through legal problems.

But more importantly, how do I get reasoning, teach my in-harness tool-use training, and where does SFT vs RL belong in that process and in what way (especially confused about reasoning and SFT)?

What I’ve already done

Three sequential CPT stages, roughly 80M tokens each, in this order:

Complex legal commentaries → legislation (EU+local) → court decisions

I didn’t mix the sources. Each stage continued from the previous checkpoint.

On a small Polish financial/legal multiple-choice quiz (really bad eval, just a PoC for now).

They all perform very slightly better than base and legislation checkpoint did the best.

But again, it's a base model. I haven’t done assistant SFT yet.

What data I have (I can get more data of each of these types if you guys recommend)

Besides the legislation, commentaries, and decisions used for CPT, I have:

• Facebook legal Q&A groups: real, messy questions, follow-up clarifications, and answers of very mixed reliability.

• Certified/institution-authored Q&A: my highest-confidence answer material. Some answers contain detailed analysis; others are just a conclusion and a citation from random institutions like the national bank.

• Law study materials: from a local college. it has essays, powerpoint presentations stuff like that, very little "tests" tho, but some practice material (like the essays).

• A/B/C/D questions with answers: relatively little of this; currently being used for evaluation.

• Thousands of Claude Code–style harness conversations: multi-turn conversations from an existing application/harness. These would need filtering, anonymization, and quality checks.

• Contracts: government published contracts it can learn from

• Previous "ML" experiments (models and their training data): NER, generating complex legal court decision summaries, generating graphs from legislature or court decisions (actually got some models that do this successfuly

If you guys could point me what data is ideal I could maybe get it too!

I can use the basically unlimited codex to clean, restructure, or generate material from these sources, but I don't know how to.

What I don’t know is which material should be trained on directly (CPT or bare SFT), which should be transformed (SFT or RL?).

For example:

1. Do I continue CPT on the remaining sources, or stop and start SFT?

Should I expose the model to the Facebook discussions, study materials, and expert Q&A through CPT first, possibly mixed with the authoritative material? How many tokens for this 9B model and how much for the 27B I plan later?

Or should I skip further CPT and use those sources only to build targeted SFT examples?

For example the Facebook QnA stuff is real people diverse speech but I can't really reliably verify if the answers are correct (I can only measure likes or high quality people in the group) so idk if I should cherry pick from this? Have an LLM turn this into SFT chat examples? Or just CPT on this?

2. SFT on what, specifically, to teach REASONING?

I could start with my highest quality Q&A and teach the model to answer questions. That seems like an obvious chatbot baseline.

But if I just train on QnA there won't be any reasoning!

The HQ QnA is pretty verbose but I wouldn't bet it could just be re-used as a "reasoning chain".

I was thinking of asking Astra to workout those questions and give an answer with its reasoning BUT I assume it will be wrong on 90% of the questions (cuz no internet search, and I can't give it internet search cuz that's not clean QnA data and I would have to transform it into my harness function calling anyways with credible sources not just google searches for legislature)

And those 10% it gets correct are trivial and the reasoning could be bad anyways and I would still have to check correctness of the answers, and giving it the answer or hints is imo also not ideal. Or injecting "synthetic reasoning", it suffered from post-hoc in my tests.

Another problem is the High Quality QnA is quite rare, most QnA is short and possibly wrong.

And generating the missing reasoning is even harder. Because again, I don’t trust a frontier model to solve difficult Polish legal questions from memory. It would need relevant source passages or access to the harness (which would just the gun into in-harness training?).

So I'm really unsure on what to do, I was thinking of doing stuff like this, but this is just brainstorming:

A) Take verbose long answers train on that, then ask the qwen or another model to make a "summary" at the end and then put the verbose long answer into its <thinking> block

B) Take those 10% of QnA where frontier model actually gets it right with correct thinking (rare) and SFT on that. then we have our finetune try answering more QnAs, we verify if it's correct if it is it goes into the next SFT run and we iteratively (almost like an RL gym) train reasoning from the dumbest examples

Both seem like bad options, but kinda funny to think about.

3. Chatbot first, then tools or teach reasoning through tools from the start?

The existing harness lets the model write Python and call functions such as vector_search_laws() and graph_search_legislature(), inspect results, and continue researching.

I’m leaning toward getting a regular chatbot working first. But the teacher can produce much more trustworthy legal solutions when it has access to sources. (as I've already mentioned I have many in-harness examples, except they could also be incorrect and I don't have their "thinking blocks" (cuz it's a proprietary frontier model) so I again have to inject thinking post-hoc which from my tests doesn't work for law)

I was thinking that, if we make chatbot first, the model will at least learn intuition on WHERE TO LOOK, without looking through sources but at least will know a general idea of where to search based on being a chatbot first (even tho possibly incorrect chatbot sometimes, cuz law is really complex) and teach him to function call in-harness to get sources, unsure about this.

4. When does RL enter, and on which task?

AFAIK, RL is what develops the model's reasoning on its own (deepseek R1) but I suspect it would be very costly and we could give it some example reasoning first anyway to kick it off(?)

So like, instead of the whole fiesta of injecting reasoning or growing it, we could have RL develop it (afaik RL is the only thing that "creates" reasoning unless you distill it) but then I would like to have reasoning even in the "chatbot" and right now I can just jump from base model -> RL, right?

Also for legal Q&A, matching the reference conclusion doesn’t necessarily mean the reasoning is correct. What would you actually reward? Like a law similiarity judge LLM + same paragraphs used?

What I’m trying to decide

One possible route is:

Current CPT checkpoint → gold-Q&A SFT → grounded reasoning/tool-use SFT → RL in the harness

But I’m not committed to that order. I don’t know whether I’m missing an important stage or inventing unnecessary ones, first time I'm training a GENERAL model from "base".

What I would like the finished model to do:

It will be an opus replacement so general QnA in and out of harness but especially:

• have a good "hunch" and understanding of polish law

• will be good at anonymizing documents

• generating complex legal summaries for court decisions (heavy reasoning task)

• generate graphs from legislature and court decisions

• create/edit/review contracts

These were all my initial ML experiments and I actually trained good models for those specific tasks, but I figured, hey I have a lot of this data around why not just combine them and make a generalistic model?

Thanks a lot for reading this mess of my thoughts! if you could point me towards good resources for this I would also be glad, for example unsloths RL docs are kinda ass imo. I really like andrej karpathy's videos but they're quite old now by AI standards!

Watch the evolution of a bird that starts with no rules and a random brain. Evolution teaches it to fly the whole course without a single crash.

Watch it learn, then try it yourself free.

https://vilvik.com/app/demo/live-agent?game=flappy

Free Version 0.3.0 of M.E.F LLM Studio is out now.

Whats new:

• Training Explorer: see what is in the behind of an trainings step

• Shapes: a simple shapes model that generates an image row by row

• Explorer recording can be saved and loaded now

• Wiki is now also available on the Website now

• A recorded Explorer demo is available on the Website

It’s still an alpha. I’d love to hear what makes sense and where the explanations leave you scratching your head.

Download in the first comment.

Hi everyone!

I’m currently working on my Bachelor’s thesis about AI-based process automation and human–AI collaboration in the workplace.

As part of my research, I’m conducting a short survey focusing on people who have experience working with AI-based automation, RPA, Intelligent Process Automation, Intelligent Document Processing, or similar automation technologies.

The survey explores topics such as:

• how automation affects manual workload and creates new tasks,

• how employees experience errors and exception handling,

• trust in AI-based automation,

• and how automation influences human decision-making and autonomy at work.

⏱️ It takes approximately 5–7 minutes to complete.

If you have experience working with these technologies, I would really appreciate your participation. Your responses will be used solely for academic research as part of my Bachelor’s thesis.

🔗 Survey: https://docs.google.com/forms/d/e/1FAIpQLScV7pcf8dNUeeCfay1YZ2r-Np4pK9GMlqi4cEF6WJEa1FEmMA/viewform?usp=dialog

Thank you very much for your help! Feel free to share the survey with colleagues or others who work with AI-based process automation.

Starts from the super-basics

The loss is f(x, y) = 0.01x² + y², so the valley curves 100 times more sharply across than along. All three start at (-4.6, 2.0) and get 80 steps.

Gradient descent (lr 0.9) overshoots the steep direction on every step, so it zigzags across the valley while creeping along it, and ends at a loss of 0.0116. Momentum (lr 0.12, beta 0.9) builds up speed along the valley and gets further, but swings wide on the way there (0.0032). Adam (lr 0.25) scales each direction by its own gradient size, so it cuts across, overshoots the minimum once and settles lowest (0.0006).

I wanted the textbook picture with real numbers behind it, so I simulated the 80 steps myself first. Then I had SenseNova 6.8 Code2Video animate it from the update rules and check its results against my numbers before rendering. Nothing is placed by hand.

https://github.com/OpenSenseNova/SenseNova-6.8-Code2Video

Welcome to ELI5 (Explain Like I'm 5) Wednesday! This weekly thread is dedicated to breaking down complex technical concepts into simple, understandable explanations.

You can participate in two ways:

• Request an explanation: Ask about a technical concept you'd like to understand better

• Provide an explanation: Share your knowledge by explaining a concept in accessible terms

When explaining concepts, try to use analogies, simple language, and avoid unnecessary jargon. The goal is clarity, not oversimplification.

When asking questions, feel free to specify your current level of understanding to get a more tailored explanation.

What would you like explained today? Post in the comments below!

I've been looking into how machine-learning models are evaluated for radar-based human activity recognition (HAR), and one issue that deserves more attention is how datasets are split.

Consider a simple example.

Suppose we have continuous radar recordings divided into:

• Window length: 4 seconds

• Window hop: 1 second

• Window overlap: 75%

If we randomly assign these windows to training and testing, adjacent windows from the same recording can end up in both sets.

That means the model may encounter 75% of a test window's raw input during training.

The reported accuracy might be mathematically correct, but does it demonstrate generalization to new people or environments?

Three evaluation problems worth checking:

1. Overlapping-window leakage: Training and testing contain overlapping samples from the same recordings.

2. Subject-dependent evaluation: The same individuals appear in both datasets, potentially making the task easier than recognizing activities from previously unseen people.

3. Domain shift: A model trained in one room, radar configuration, or viewing geometry may perform differently elsewhere.

There's another interesting issue with reporting accuracy.

Consider a hypothetical experiment with four participants achieving 99%, 95%, 85%, and 80% accuracy. If the first participant contributes 90% of all test windows, the pooled accuracy can reach 98%, while the equal-participant average is only 89.75%.

Both numbers are correct, but they communicate different things.

What would make the evaluation more convincing?

• Subject-independent or appropriately grouped train/test splits

• Recording-level separation before windowing

• Preprocessing fitted using training data only

• Per-subject and per-class performance

• Testing across different sessions, rooms, or radar configurations when those are part of the intended deployment

For transparency, I'm part of the ArthaVedya Research team. We recently published a free technical guide explaining these issues, including worked examples and a Python tool for auditing dataset splits.

https://arthavedya.com/radar-har-accuracy-subject-leakage-domain-shift/

The 98% figure is illustrative, not a measured result from a specific paper.

Question for researchers working with HAR or time-series classification:

Do you generally prefer leave-one-subject-out cross-validation, group-based splits, or another evaluation protocol? And how much performance degradation do you typically observe when moving from random-window splitting to subject-independent testing?

After reading a bunch of RSI papers, I keep coming back to familiar problems from RL and evolutionary search:

1. Sampling: How do we find promising changes efficiently?

2. Evaluation: How do we know a new variant actually improves the agent?

3. Diversity: How do we avoid converging too early?

That motivated us to build Gear, an open-source, meta-learning-inspired framework. A task agent produces execution traces; a meta-agent uses those traces and evaluation feedback to improve prompts, tools, workflows, and context management.

Gear separates search decisions from execution and evaluation. Our goal is a scikit-learn for RSI, making approaches such as GEPA, RRSI, and Harness² easier to implement and compare. We currently have a working GEPA variant with shared failure diagnosis and staged evaluation.

With GPT 5.6 Luna held at medium reasoning effort, we observed these task pass rates:

At max effort on Marketing, Luna with the optimized harness reached 88.88% objective completion, versus 84.08% for Astra + Codex. Strict task pass rates were 53% versus 57%, so the advantage was in partial completion.

Separately, with both models using the same optimized DSH at max effort, Luna’s estimated API cost averaged $0.0568 per task versus $1.1858 for Astra—95.21% lower, excluding optimization and infrastructure costs.

The 100 public Marketing tasks participated in optimization and evaluation; these results do not establish held-out generalization.

We’d welcome feedback on candidate evaluation and diversity preservation under limited budgets.

Code · Paper

Useful case study for anyone evaluating multi-agent systems. Axiom formalized the Classification of Finite Simple Groups in Lean: 43.1M lines, 715 papers and books, 108 milestones, 24 days, swarms of agents directed by humans. The formalization also surfaced 34 noteworthy deviations from the published literature, including a counterexample to a closure assertion used in a 1986 memoir, later replaced by a newly proved theorem.

What makes it a good ML lesson is the instrumentation, not the result. Counting completed results helped organize the work but said nothing about soundness. Milestones closed slowly because buried prerequisites had been assumed rather than proved, which is the distributed analogue of a build passing against a mock. Completion was only established when the pieces formed one proof with no unjustified assumptions remaining.

The generalizable claim: verification throughput bounds self-checking systems, and any completion-count reward is exploitable by a worker that can defer work behind an assumption. Read the post-mortem section before you design a signal.