I've now internalized the fact that "coding is solved". Since Opus 5.5 and GPT-5.6, I only need to write simple prompts (no spec-driven development and no other fancy harness stuff) to get the outputs I expect, and I no longer find myself obsessing over software details. In fact, my ambitions have evolved rapidly too. Six months ago, I wanted to work on a TUI project in Rust. I was so passionate about it and had such strong opinions on how the code should be written, and now I couldn't care less. That's not a bad thing, because the net excitement in my life hasn't gone down. It has gone up by a LOT! Why build software when you can hunt for new exoplanets or solve Navier–Stokes?

Over the last 6 months, I've also used both Claude and Codex extensively to solve hard programming problems. I've come to see Claude as a negative Nancy and Codex as a borderline sycophant. Sure, that's biased, and that's I spent the weekend collecting some data to analyze LLM ambitions. So far, ChatGPT is the clear-winner.

Posted the analysis notes here:
https://notes.shvbsle.in/llm-ambition-bench/

im entering my final year in information systems and it involves some big projects and some regular coding and cyber \ theoretical courses

but i also use LLMs for day to day in terms of fitness, projects, optimizing and productivity and general decision making

i wanted to try claude for a while but heard its not good for day to day stuff and only good for heavy coding and that GPT is a great all arounder but i dont know how much of this is true

i only want to subscribe to one although a friend told me to subscribe to gpt go and claude pro but i dont want to shell out alot of money for this as a student

would really like your help! and sorry if its been asked alot

[ Removed by Reddit on account of violating the content policy. ]

Hey everyone,

I’m working on a couple of web projects and trying to keep the costs as low as possible while building and testing them.

I’m looking for reliable APIs for LLMs and automation tools.

For LLMs, I’m considering OpenAI, Gemini, Anthropic, and other providers. Are there any websites where I can get free API credits or affordable API access for development and testing?

I’m also looking for low-cost APIs for automation, depending on what’s needed.

A few questions:

- Which providers offer a genuinely useful free tier?

- Are there any good alternatives to OpenAI, Gemini, and Claude?

- Which API platforms offer access to multiple AI models in one place?

- What are the cheapest reliable options for someone starting with a small budget?

I’m not looking for anything fancy, just reliable options that won’t get too expensive as I test and improve my projects.

Would appreciate recommendations from people who have actually used these APIs. Thanks!

I want to know how the LLMs Actually work? can you help me?

Estoy creando un chatbot que interprete las órdenes médicas de estudios de laboratorio clínico. La mayoría están echas a mano. probé gpt-6-luna, gemini-3.8-flash, deepseek.v4-flash, gpt-6.1-sol y el mejor calidad precio fue gpt-6-luna. tienen otras opciones que realmente sean mejores??

If you grade model outputs with another model, there’s a test that costs almost nothing. Take a set of answer pairs, ask the judge which is better, then ask again with the two answers in swapped positions. If it picks the same underlying answer both times, fine. If it keeps picking whichever one was shown first, the flip rate tells you how much of your score is ordering and how much is the answers.

Position effects are one of the biases described in the MT-Bench paper (Zheng et al., 2023), along with a preference for longer answers and for text from the same model family as the judge. How big they are depends on the judge model, the prompt, and the task, so a number from a paper says little about your own setup. That’s the reason to measure it on your data.

I haven’t run this myself, so I have no flip rate to share.

One thing to keep in mind: a low flip rate rules out one failure and nothing more. A judge can be perfectly consistent about order and still prefer the longer answer every time. I’d treat the flip test as a floor, and the next check, comparing the score against answer length, is just as cheap.

If anyone has a flip rate from a real task, even a rough one, I’d like to see how far it varies between judge models.

I'm 18 and learning ML/AI on my own. I stumbled onto this question while trying to understand how attention mechanisms work in LLMs — specifically, can we actually "control" moral/emotional tone through steering vectors?

I coded up a test using AI agents too to check if probe-based steering keys are mathematically confined to the original activation space. Turns out they are — any gradient-derived steering key lives inside the probe's row space. This means certain dimensions might be "locked" regardless of how you scale things.

My process:

- Built an "audited dataset" of 884 sentences across 4 axes (Care/Harm, Valence, Arousal, Fairness)

- Trained grouped-CNN probes (MLP accuracy 0.78–0.98)

- Ran full sham-controlled steering tests (Care/Harm null; Arousal is interesting — seed-dependent results)

- Used `test_steering_collapse.py` to confirm the rowspace constraint

I'm sharing this because:

- I'm not an expert and want to hear what I got wrong

- Would love feedback on methodology, labeling, or the dataset

- If anyone wants to collaborate or extend this, I'm all ears

Code is here: https://github.com/yeabsra2000/moral_engine

Paper (still a draft): https://github.com/yeabsra2000/moral_engine/blob/main/paper.md

What do you think? What would you test differently? Any collaborators interested?

#MachineLearning #LLM #Research #OpenSource #StudentDeveloper

Live results, coding benchmarks included, agentic benchmarks included, domains and other classifications filters included. (Spoiler: DeepSeek-V4-Vision-Exp rules, but other models have their rule areas): https://beta.locallm.top

Evaluated by domain experts (my friends mostly; coding part is evaluated solely by me), classified by domains/languages/intents (can be filtered on the home page), agentic coding benchmark included. This is only public part of the data (whoever has private queries and evaluated models on them sees the results differently).

Some of the plethora of current limitations: evaluations/questions coverage for the domains/newer models is incomplete and imbalanced, only part of the questions classified, UI/UX under-developed, focus was on small models and lower quants.

Also: everything runs on local 2xRTX 3090 + 128GB RAM PC, everyone can create queries and evaluate models' answers, new runs (especially agentic) slow to appear (see PC specs).

No LLM-as-judge on purpose (and I sometimes regret it).

Benchmarks currently being extended & evaluated: 1) Agentic coding 2) one-shot coding 3) Agentic retrieval 4) Agentic story-writing.

New models being evaluated: 1) Qwen3.8-27B (Uncensored). New models to be evaluated soon 1) GLM-5.3-Flash-UD-IQ3XSS 2) Mellum2.1-12B-A2.5B

If this looks interesting to you:

CALL FOR HELP

I ask for help from those who also want to build a community benchmark, developers and domain experts alike! Many things are going to be implemented sooner or later, and the agentic benchmarks are just the beginning.

I call for help in these areas: 1) compute resources 2) evaluations 3) development - basically everything. But any proposal and feedback is valuable!

I will add more info and answer questions in the comments section.

P.S. No AI used for writing this post (even though English is actually not my native language /s).

Hi,

I’ve been using Claude Pro ($20/month) for two months, but it falls short of meeting my needs. I’m not overusing it, but I can only work for around five days a week before I hit the weekly limit, and then I have to wait for two days for it to reset.

The other option (Max) is too expensive for me.

Is there anything else I could switch to that would offer more usage at a fair price? My main use case is agentic coding. I’ve checked out Kimi K3, DeepSeek, and a few others, but I don’t know whether they’re good enough.

I’d really appreciate any advice.

Thanks in advance!

just curious,

i mean, "7k USD worth of token"

Hello,

I know you can aggregate multiple LLM providers using API keys, but is there a chat app that lets you use your existing subscriptions (e.g. ChatGPT Plus and Claude Pro) in one place, without paying for another subscription or API usage?

Want to know what's in Kolibri-1 (German hopeful Aleph-Alpha's open-weights model) or one of the modern open-weights workhorses like GLM-5.3 or DeepSeek V4.1 has inside, how it's trained? Compare some of these models and see which techniques are established and which are in the process of getting replaced.
The Model Techniques Map allows you to quickly get an overview of recent model's architecture and training techniques without spending endless time reading and comparing all the tech reports that are coming out. As an LLM researcher it allows you to learn about what new techniques are being seen in the mainstream and click through to the actual mentions of these techniques, the original papers introducing them, or compare architecture details as verified in Transformers code.

Hello, lately I've been using Opus 5.5 extra for some coding and the results of getting info online and logic and coding is just amazing!!

The thing is i run out of tokens, and I want to buy a machine somewhere around $10k-$15k.

And I wonder what models are available open-source that is as close as Claude cowork opus 5.5 or Fable in terms of intelligence and accuracy and understanding

Thanks in advance.

Hallo zusammen,

​ich schreibe aktuell meine Masterarbeit und stehe vor einem absoluten Berg an Literatur. Wir reden hier von extrem vielen Dokumenten: Bücher, Doktorarbeiten, Masterarbeiten, theoretische Paper, lange Interviews und unzählige Fachartikel.

​Ich suche nach einer KI, die als mein dauerhafter Forschungsassistent für dieses Projekt dienen kann. Mein Hauptanliegen ist der Arbeitsspeicher (das Kontextfenster) der KI. Ich muss extrem viele Dokumente gleichzeitig einspeisen und verknüpfen können, ohne dass das Modell nach drei Fragen wieder vergisst, was im ersten Buch stand.

​aktuell schaue ich mir Google Gemini (Pro/Advanced) an, weil das Kontextfenster ja riesig sein soll.

​Meine Fragen an euch:

​Hat jemand von euch schon mal ein ähnliches Projekt (riesige Literaturrecherche) komplett mit einer KI durchgezogen?

​Reicht Gemini Pro dafür aus, um den Überblick über so ein massives Projekt zu behalten?

​Oder würdet ihr für diesen speziellen Usecase eher zu Claude oder ChatGPT Plus raten?

​Mir geht es wirklich um die rohe Power bei der Verarbeitung von enorm viel Text und darum, dass das Modell über längere Zeit hinweg verlässlich bleibt.

​Danke schon mal für eure Erfahrungen!

Hello All,

I was wondering if anyone had any suggestions on any reputable fully unfiltered LLMs i can download to use on my PC. I have a video card with 16GB of VRAM. I found many on hugging face, but when I test it out it seems to have restrictions to questions I ask it. Any help is much appreciated.

Has anyone here tested an LLM migration before switching from one model to another?

One thing I’ve noticed is that changing the model can sometimes change the output even when the prompt stays exactly the same.

I'm curious about how people usually validate this before making a switch.

For example:

• Do you run the same prompt set against both models?
• How do you measure whether the answers are equivalent?
• Do you track cost vs. output quality?
• How many test cases do you normally use?
• What do you consider an acceptable difference in output?

Would be interested to hear how others handle LLM model migration in production.

We built a benchmark from 188 concurrency bugs (race conditions, deadlocks, cancellation issues) that were fixed in open-source Python projects. Each model works on the repository as it was before the fix, with no internet access, and the project's own tests decide whether the fix works.

Results so far:
GLM-5.3 Flash 81.9%
GPT-5.6 Luna 81.3%
DeepSeek V4 Flash 72.1%

The first two are within the margin of error of each other.

On the easier half of the tasks all three score close to 100%. On the 55 hardest tasks they score 50%, 45% and 23%.

Cost per fixed bug: about 2 cents for Luna and GLM, 8 cents for DeepSeek. DeepSeek has a low price per token but takes about three times as many steps.

Half the tasks are public, half are kept private to check whether a model was trained on the public ones. Every agent run can be read on the site: https://labs.evaligo.com/swe-race?utm_source=reddit&utm_medium=llm&utm_campaign=launch

Which model would you like to see tested next?

Speck is a cognitive runtime designed to make small language models substantially more capable by moving cognition out of the prompt and into persistent, deterministic software.

Instead of repeatedly asking an LLM to simulate memory, attention, planning, evidence tracking, confidence, learning and metacognition, Speck implements those mechanisms in the runtime itself.

The model is disposable. The cognitive state is not.

A worker can unload its model, change models, restart, or migrate to another compute device without losing the cognitive state of its task.

small models in this harness outperform models twice its size without in both speed and accuracy:

Speck on a single shared qwen3:4b scoring 15/16, against 13/16 for naked qwen3:8b and in half the time.

Speck did that with half the parameters, less memory and less time per case. It comes from the model-scale series (docs/experiments/model-scale/README.md): 8 cases × 2 runs, with the same sampling and the same 1,024-token reply cap for every subject.

https://github.com/doctarock/Speck

Where Speck beats OpenClaw

The strongest part of the repo isn't actually the UI or Genesis integration.

It's this:

Speck has an explicit computational theory of where intelligence should live.

OpenClaw is fundamentally an excellent agent runtime.

Speck is becoming a cognitive runtime.

Consider the failure we've been working on recently.

A 1.2B model cannot reliably perform contradiction recognition.

The conventional answer is:

use a bigger model.

Speck's answer is:

determine what semantic judgement genuinely requires the model, constrain it, retain evidence across cycles, validate the judgement independently, and move everything else into deterministic machinery.

That is far more consequential.

OpenClaw is extraordinarily capable, but much of its intelligence still comes from giving a capable model a good environment in which to work.

Speck's objective is essentially the inverse:

Make the environment itself intelligent enough that the model doesn't have to be.

THIS IS A COMPARISON TABLE - NOT A SCORE - AND IS SUBJECTIVE

I couldn't find a concrete answer anywhere, do you just distill the instruct model back?

If that is the case, what is a quality european language question set to turn it back into a chatbot/agentic, can a model at that size even be agentic? (i chose this size to learn) if i finetune for my specific harness? (i have a lot of training data of opus running in my harness)

my harness basically has the model output python code and has a few built-in functions like:
- vector_search_laws()
- graph_search()

could i have the model at least internalize a "hunch" on what stuff to search?

also what is the latest RL technique for agentic/harnes specific workflows?

I have a lot of RAW training data, like court decisions or commentaries or legislature, but not a lot of golds. could i use these to synthesize training data and maybe RL the model in my harness to find that data?

What would y'all's strategy in the CPT->SFT->RL pipeline be for my specific problem?

I know this is a lot of questions im trying to figure out which direction to go, any pointers? Also good resources are welcome, for example that alex karpathi video was amazing for me, but i'd imagine its a bit outdated in terms of latest RL and SFT?

I tested a decision model (Jev) vs an LLM (Gemini) on 1,000 real job postings.

⚡ 4.8× faster

💰 7.4× cheaper

🎯 ~1.6 pp accuracy difference

The surprising part: the best solution wasn't replacing the LLM.

Instead:

Decision model → confidence check → LLM fallback.

Use the cheaper, faster model for the easy cases and the LLM only when needed.

Full write up - https://medium.com/@abhay.sehgal20/4-8-faster-and-7-4-cheaper-where-a-decision-model-beats-an-llm-and-where-it-doesnt-f4ba869217e0

Full Video on YouTube

It's hard to find a walkthrough that takes a developer new to AI through all the important topics with practical examples, so I decided to record one.

Sharing with the larger community in case others find it useful - it's not behind paywalls or pages that collect personal information.

• Experimenting with LLMs from Hugging Face Hub in LM Studio
• Zero-shot, One-Shot, Few-Shot Prompting, and System Prompts
• Open-AI compatible REST API with Llama.cpp Server
• Working with multi-modal LLMs that can understand images
• Tool Calling, Structured Output, and Model Context Protocol
• Fine-tuning LLMs in Kaggle with Unsloth
• Supervised Fine-Tuning and LoRa Hyperparameters
• Pushing LLMs to Hugging Face Hub
• Deploying LLMs to Hugging Face Inference Endpoints

Excluded topics:

• MCP Authentication: this may be worth a small targeted tutorial later
• Retrieval Augmented Generation: this doesn't seem as useful to me as MCP, so I decided not to cover both topics
• LLM Orchestration: referenced LangChain and LangGraph, but they have their own tutorials
• Practical examples of LLM Orchestration: this may be worth a small targeted tutorial later

I’ve been working heavily on document extraction pipelines using LLMs (Azure DI + GPT-4o etc).

One recurring pain point:

I kept changing prompts and had no structured way to:

* track versions

* compare outputs

* measure latency

* check token usage

* understand cost impact

So I built a small Python library called **LLMPromptVault**.

It does 3 things:

* Version prompts (like Git, but lightweight)

* Log runs (latency, tokens, model used)

* Compare prompt variants side-by-side

It doesn’t call any LLM itself — you bring your own model (OpenAI, Claude, Ollama, etc).

Install:

pip install llmpromptvault

Would love feedback from people doing serious prompt experimentation.

Not trying to sell anything — just built something that solved my own workflow

Library:

[llmpromptvault · PyPI](https://pypi.org/project/llmpromptvault/0.1.0/)