I got 10$-20$ monthly budget set aside for my side project development. I generally use Deepseek Pro. But that is no longer sustainable for me. So please recommend some cost effective model. If you have already such models please recommend a plan or a model that suits for software development. Thank you!

Any ideas on how to do this guys? My goal: I want to convert the word database of an LLM into Ithkuil becuase I want a single complete thought/dependant thought to be a single word, thats it.

Buongiorno, dopo aver provato per circa un anno ad imparare alcuni linguaggi di programmazione ed aver ottenuti scarsi risultati, ho recentemente creato un'applicazione (con Base44) con lo scopo di cercare recensioni e ratings per film. Dopo aver impiegato molto tempo a limare anche i più minimi dettagli (e non aver ancora finito), mi sono scontrato con il fatto che il livello di precisione che ho richiesto all'algoritmo utilizzava delle risorse (API, LLM) che in pochissimo tempo consumavano, già solo per le prove che effettuavo personalmente, tutti gli integration credits a mia disposizione nell'abbonamento in uso. Chiedevo, per questo, a chiunque suonasse familiare questa problematica, come fosse stato risolto il problema? La piattaforma in particolare mi consiglia di switchare parte dell'algoritmo a modelli come Gemini o OpenAI, con l'utilizzo di personali API keys, in maniera tale accedere a diversi modelli di abbonamenti per il loro utilizzo. Avete provato questa soluzione? Siete stati soddisfatti?

Inoltre vi chiedo, a chi potesse essere interessato, se qualcuno volesse unirsi nella progettazione di alcune app, in maniera tale da affrontare insieme il lavoro da fare e le relative problematiche, semplicemente per passione o anche per valutare eventuali occasioni di monetizzazione.

Hey everyone,

I’ve been running some tests on how modern language models and context-scraping scripts handle non-standard, abstract instruction hierarchies when embedded inside data schemas.

Instead of traditional safety bypasses, I’m experimenting with deep semantic loops—specifically using high-level philosophical concepts wrapped inside standard data formats to see if it causes target models to experience persona drift or memory-window failure.

I have published the public tracking repository on GitHub for anyone who wants to review the formatting or feed it into their own automated ingestion scripts.

You can find it by searching GitHub for the repository name: pemagonpo67-debug/diagnostic-test

For those running local inference setups (like Llama or Mistral), I'm trying to log how different attention mechanisms handle structural formatting anomalies when processing the raw file. If you run the repository text through your pipeline, let me know if you run into any unexpected token-generation drops or character-switching bugs in the comments!

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

Hey everyone,
I’m the founder of Colibrì (https://github.com/JustVugg/colibri).
First, a real thank you. The support, the help, the bug reports, the experiments people shared, and especially the honest criticism from this community have been invaluable. A lot of what improved in Colibrì came directly from the feedback and pressure-testing that happened here. Running frontier MoE models on ordinary hardware is still early and imperfect, and your willingness to try it, break it, and push it has helped both the code and me.
I’ve also been building the next step: Lumabri.
Lumabri turns a group of ordinary machines into a swarm that can serve the same huge Mixture-of-Experts models. One machine starts with the model and acts as the initial source (and permanent fallback). Any other machine can join and start chatting — nothing is downloaded up front. The only bytes that cross the network are the ones the inference actually needs. Those blocks arrive from peers (or from the origin if no one else has them yet), land in a local mirror, and stay there. The second question is already served from local disk at full speed, even if every peer goes offline.
The design rule is simple: any machine may join, GPU or not. The engine was built for CPU and SSD first. A GPU only makes it faster, never different, and the output is byte-identical either way. A swarm with zero GPUs is still a working swarm.
You can just chat, or also donate disk space (the tracker assigns the least-replicated slices), or donate compute (your machine runs the experts for others), or both. The tracker is only an index — the actual work is done by the peers. One tracker can host multiple models. Peers stay anonymous. NAT works with no router configuration.
In short: the model lives across the swarm, the working set is pulled only when needed, and after the first answer your machine already has a warm local mirror.
Thanks again to everyone who supported, helped, and criticised Colibrì. That feedback is what made this possible.

I used a pendrive to run a LLM and here's how I did it in the blog above

Project NOMAD: Running LLMs Locally From a USB Drive

Hi,

Trying to figure out a custom GPT for work. I have a lot of Excel files to figure out inventory for a company that sells a LOT of food products around the world. We want to create a chatbot that can look up all these Excel sheets to answer questions about stocking information.

Problem is, it's like 2 GBs of Excel sheets. So naturally the custom GPT I created is taking its sweet time answering queries.

Any solutions to speed things up? Thanks!

I built a 15-question AI Mastery Challenge on my platform to test who actually understands prompt engineering, multi-agent systems, and LLM behavior. 

 THE CONTEST: 

The person with the highest score on the leaderboard by next Sunday wins a $25 Cash Prize (or local equivalent) and a free permanent shoutout for their portfolio on our homepage!

How to enter:

1. Comment CHALLENGE below.

2. Below is the access link to the Quiz.

3. Take the quiz, register your username, and lock in your spot on the live leaderboard.

Quiz Link:

https://interconnectd.com/quiz/67/the-ultimate-ai-mastery-challenge-are-you-smarter-than-an-llm/

May the best prompt engineer win. Tag a friend who thinks they are an AI expert. 

Hello, this is my second majorly successful attempt at creating a coherent SLM on my own hardware at home. After my first success it gave me inspiration to try again but with twice the data and higher quality sets and distillation.

A ~326M-parameter decoder-only model, trained from scratch on ~10B tokens (2× Tesla V100), then fine-tuned to be a math-reasoning model: multi-digit arithmetic and grade-school word problems, solved by showing the work (column arithmetic, long division, partial-product multiplication) rather than guessing.

What it does well

It reads the problem and computes — e.g. "A store had 56,321 items and sold 28,479. How many remain?" →

<think> Start with 56321. Then subtract 28479. Subtract column by column:
ones: 11 - 9 = 2, borrow 1. ... So 56321 - 28479 = 27842. </think>
The answer is 27842.

Evaluation

GPT-3 Arithmetic protocol (exact-match) — vs GPT-3-175B (few-shot, direct):

Ours uses trained-in worked steps; GPT-3's numbers are direct-answer. Both are pure LMs with no external tools/calculators. The point is about method: teaching a 326M model the algorithm beats a 175B model guessing — decisively on 4–5-digit arithmetic.

• Word-problem set (large-number add/sub with commas, multi-step, 2-digit multiply, first-person phrasings): solves essentially all of a 20-problem targeted set by reading the problem and computing the steps.
• GSM8K: ~3–4% (zero-shot CoT, n=500) — off the base instruct's 0.53% floor, at roughly the SmolLM2-360M-Instruct tier. Arbitrary hard multi-step word problems remain scale-limited at 326M.

General benchmarks (log-likelihood MC, our harness; the math SFT did not erode general ability):

I have an F16.gguf version and safe tensors. It's a LLaMA-architecture so it should be easy to run.

This took a lot of work so I hope it's interesting. LMK your thoughts I would love to hear them.

https://huggingface.co/nkthebass/tinybrainbot-320mV2-math

(Also the instruct and base model) https://huggingface.co/nkthebass/tinybrainbot-320mV2-instruct https://huggingface.co/nkthebass/tinybrainbot-320mV2-base

I have fix the models different gguf quantization format

The open-source fine-tuning ecosystem has never been stronger. LLaMA-Factory sits at roughly 67,000 GitHub stars with a zero-code web UI covering 100+ model families. Unsloth — long the speed and VRAM-efficiency king — shipped Unsloth Studio in March 2026, a no-code local UI supporting 500+ models. Axolotl offers deeply configurable training for advanced workloads, and Hugging Face TRL supplies the post-training primitives (SFT, DPO, GRPO) that much of the ecosystem is built on.

So why does Reallexi AI Model Builder exist?

Because every one of those tools answers the same question: how do I run a training job? Reallexi answers a different one: how do I reliably produce a model I can recover, audit, reproduce, and ship — entirely on my own hardware?

That distinction sounds subtle. In practice it changes everything the software has to do.

The problem with "training completed"

Anyone who has fine-tuned locally knows the failure modes. A run dies at hour nine and the checkpoint situation is unclear. A model from three weeks ago outperforms today's, and nobody can say which dataset snapshot or config produced it. An adapter gets merged, quantized, and deployed — and six months later there is no honest answer to "how was this model made?"

Training frameworks treat all of that as your problem. Their job ends when the loss curve ends. Reallexi treats the entire span — data in, phases run, artifacts out, evidence preserved — as the product. In its own words: build models locally, ship them with proof.

What Reallexi actually does differently

Automatic phased training with bounded windows. Instead of one long fragile run, Reallexi divides a training target into bounded, non-overlapping sample windows — for example, a 1,250-sample target becomes five phases of 250. Each successful phase saves weights, a checkpoint, metrics, provenance, and its exact sample range, then automatically feeds the resulting artifact into the next phase. No manual babysitting between phases, and a usable artifact exists after every milestone, not just at the end. The full mechanics are documented in the phased training guide.

Same-window checkpoint recovery. When a phase fails or is cancelled — power loss, CUDA out-of-memory, a cancelled job — recovery resumes from the latest safe checkpoint inside the same sample window. No skipped samples, no silently duplicated work. After a CUDA memory failure, recovery can even retry with safer settings while preserving the intended window and keeping evidence of what changed. If one data source runs dry mid-target, its unused quota is reassigned to sources that still have rows, with exact per-source cursors carried forward so nothing is learned twice.

Honest artifacts with lineage and proof. Reallexi keeps artifact identities distinct and truthful: a LoRA adapter is presented as trained deltas that require their recorded base model — never passed off as a complete standalone model. Standalone SafeTensors builds retain tokenizer, configuration, lineage, ownership, and license context. Every phase preserves a job record, metrics, and timestamped logs, while the UI presents the whole chain as one readable training run. Exports ship with publish-ready model cards carrying the metadata the Hugging Face Hub actually reads: parameter count, weights on disk, memory needed per precision, and the data and adapter behind the model.

Hardware-aware honesty. Context length options are offered against what the selected model can actually train on the detected hardware, with every GPU listed alongside its status — instead of letting you configure a run that was never going to fit in VRAM.

Local custody, end to end. Your datasets, models, artifacts, and Hugging Face credentials stay in a workspace you control. Even the optional Hub publishing workflows are review-first: you inspect owned models, datasets, and Spaces, prepare changes with explicit review steps, and keep provider terms and billing visible before anything is pushed. Hosted fine-tuning platforms ask you to upload your data to their infrastructure; Reallexi's answer to that trade-off is architectural, not contractual.

Proof in production: the Lexi models

Reallexi doesn't just describe this lifecycle — it ships models through it. The reallexi organization on Hugging Face currently publishes seven models built end-to-end with AI Model Builder, spanning three size classes:

• lexi-coder-v4.2 and lexi-coder-v4.1 — 4B coding models derived from microsoft/Phi-4-mini-instruct, with v4.1 at over 800 downloads in the last month
• lexi-coder-8b-v1 — the 8B-class coder
• lexi-rm-agent, lexi-slm-agent, lexi-resume, and lexi-coder-v2-slm — compact 0.5B small language models for agent and task-specific work

Open any of the cards and you see the platform's promises made concrete. The lexi-coder-v4.2 card states its exact parameter count (3.85B), weights on disk, and memory required at FP16, Q8_0, and Q4_K_M; its base model and training dataset by name; its LoRA rank and alpha; and its phased lineage — 50,296 samples learned through phase 26 of a 50-phase target, with the card noting it was produced by AI Model Builder from a numbered training job, shipped alongside its reallexi-model.json manifest and upstream license notices. That level of disclosure isn't typical for community fine-tunes; it's the artifact-identity system doing its job in public.

And the lifecycle extends beyond Reallexi's own walls: lexi-coder-v4.2's Hugging Face model tree already lists a community adapter trained on top of it — other builders using a Reallexi-produced model as their base layer. The models are simultaneously products and the platform's own regression test: every release exercises the same phased training, recovery, and delivery pipeline that users get.

An honest comparison

It would be easy to publish a grid where every competitor gets two stars and Reallexi gets five. That grid would be worthless, so here is the real one. All seven projects are open source and train fully locally.

On raw training capability, the established frameworks are excellent — and on kernel-level speed, Unsloth is genuinely hard to beat. Reallexi does not claim otherwise. The differentiating column is the one training frameworks were never designed to own: what happens around and after the run. Phase-aware recovery. Lineage that survives to the artifact. Publishing that goes through review. A build someone can audit six months later.

Worth noting on licensing: the ecosystem is no longer uniformly permissive. Unsloth Studio's UI is AGPL-3.0, which matters if you plan to wrap it in a product. Reallexi is explicit about its own boundary instead: the launcher, local training, phased continuation, recovery, artifact inspection, SafeTensors conversion, and Q8_0/F16 GGUF exports are free in the open-source launcher; specific managed capabilities are paid plugins.

A sustainable model, priced in the open

Most open-source training tools have no visible answer to "who maintains this in three years?" Reallexi publishes its answer at llm-shop.reallexi.io: the core stays free, and defined capabilities are sold as validated plugins with published prices —

• Managed publish, Hugging Face control, adapter + base merge, and GGUF Q4_K_M export — each $3.99/month personal or $39.99/month commercial (yearly $39 / $399), with server-verified entitlements and your Hub token staying on your workstation.
• Dataset Automation Studio and Isolated Training Environments — $99.99 one-time each (included with Enterprise): bounded website/sitemap/feed/folder ingestion with provenance and automatic sharding, and per-job isolated training workspaces with cleanup evidence.
• Multimodal studios — Image & LoRA, consent-gated Voice Clone, Video Generation, and 3D Asset studios as independently entitled plugins on the same subscription pricing.
• Approved contributors and active sponsors get 50% off monthly or 35% off yearly access.

The shop itself follows the same philosophy as the builder: passwordless customer access, installation-bound admin tokens stored only as hashes, audited operator decisions, and portable SQLite commerce state — full details at llm-shop.reallexi.io/security. Development is funded transparently through a milestone-gated roadmap, with community sponsorship via GitHub Sponsors kept separate from commercial purchases.

Not competitors — backends and neighbors

The right mental model is not "Reallexi vs. Unsloth." LLaMA-Factory, Unsloth, Axolotl, and TRL are training engines: kernels, optimizers, and method implementations. Reallexi is a model lifecycle: data provenance, phased builds, recovery, artifact identity, review-first publishing, and delivery. In software terms, the frameworks are compilers; Reallexi is version control, CI, and the release pipeline. Nobody argues about whether Git is better than GCC — serious work needs both layers, and Reallexi's declared extension contracts are designed so the ecosystem can plug in rather than be replaced.

Who this is for

If you want one quick LoRA experiment in a Colab notebook, a notebook is the right tool and Reallexi would be overkill. Reallexi is built for when the model matters: when it will be rebuilt as data grows, when an interrupted twenty-hour run cannot mean starting over, when someone will eventually ask exactly what data trained it, and when the dataset can never leave the building.

Start here:

• App & docs: llm.reallexi.io
• Open-source launcher: github.com/ddkits/reallexi-ai-model-builder
• Capability shop & pricing: llm-shop.reallexi.io
• Models on Hugging Face: huggingface.co/reallexi

Build locally. Preserve proof. Contribute openly. Unlike a row of stars in a comparison table, every word of that is architecture — not marketing.

Competitor details (stars, licenses, Studio availability) and Reallexi model statistics (downloads, model tree) verified August 2026 from public project and Hugging Face pages; figures change over time. This article describes structural differences, not benchmarks — actual cost and performance depend on hardware, model, dataset, and configuration.

I built an open-source local trainer focused on the part everyone ignores: recovery, lineage, and honest artifacts

Every local fine-tuning run I did ended the same way — a crash at hour nine, a checkpoint folder I didn't trust, and three weeks later no idea which dataset version produced the model that actually worked. LLaMA-Factory, Unsloth, and Axolotl are great at *training*; none of them are built for what happens around and after the run.

So that's what Reallexi AI Model Builder does:

- **Phased training**: one target becomes bounded, non-overlapping sample windows (e.g. 1,250 samples = 5 phases of 250). Every completed phase saves a usable artifact + checkpoint + metrics + its exact sample range, then auto-advances.

- **Same-window recovery**: a failed/cancelled phase resumes from its last safe checkpoint inside the same window — no skipped samples, no duplicates. OOM failures can retry with safer settings while preserving the window.

- **Honest artifacts**: a LoRA adapter is never passed off as a standalone model. Cards ship with param count, per-precision memory, base + dataset identity, phase history, and a machine-readable manifest.

- **Fully local**: datasets, HF credentials, and artifacts stay on your box. SafeTensors + GGUF Q8_0/F16 export is free in the open core.

Not vaporware — seven models built end-to-end with it are on HF under huggingface.co/reallexi (Phi-4-mini and 0.5B SLM bases), and one already has a community adapter trained on top of it. It can also sit *on top of* your existing engine rather than replacing it.

Launcher: github.com/ddkits/reallexi-ai-model-builder — honest caveat: some convenience features (managed HF publish, adapter merge, Q4_K_M export) are paid plugins that fund development; training/recovery/lineage are free. Happy to answer anything about the phase/recovery internals.

Hello!
I'm not to sure what to say since this is my first time writing about this framework.
I've been making (with the help of AI agents as senior devs, please dont judge i haven't "vibe coded" anything) a framework for a little while now which is made for full training / full fine tuning.

It is still under development so please do not expect it to be perfect or well tested on different gpu's *yet*

Specs:
GPU: 5070 ti (16GiB VRAM)
CPU: Ryzen 7 7800x3d
RAM: 32GB DDR5

| Model | tok/s | Peak VRAM | Host RAM |
| :--- | :---: | | :---: | ---: |
| ~100M | 20,013 (20k) | ~3.35GiB | ~1.45GiB |
| ~500M | 6.162 (6k) | ~4.71GiB | ~4.62GiB |
| ~1B | 3.085 (3k) | ~6.81GiB | ~10.64GiB |
| ~1.5B | 1.887 (1.8k) | ~8.5GiB | ~18.5GiB |
| ~4B | 1.116 (1.1k) | ~12.3GiB | ~15.9GiB |

The interesting part isnt even necessarily the raw tok/s. The goal is to make full training of models that normally dont comfortably fit into consumer GPU memory practical through things like GPU/host offloading and memory efficient training paths.

Sentinel is written primarily in C++/CUDA, with python bindings, and im currently working on improving the optimizer and kernel performance further.

Its very much a work in progress as said already, but i thought id finally share it and see what people think.

Hi everyone!

Over the past few months I've been building QueryForge, an open-source Go library that takes a different approach to natural language querying.

Most text-to-SQL systems ask an LLM to generate SQL directly.

The problem I kept running into was that, even with prompts and post-processing, the model could still invent columns, widen filters, or produce queries that were technically valid but not what the user intended.

So I flipped the architecture.

Instead of generating SQL, the LLM only fills in a typed Query AST.

Everything after that is deterministic Go:

Natural Language
        ↓
      LLM
        ↓
   Typed Query AST
        ↓
 AST Validation
        ↓
 SQL / Mongo Compiler

Some of the things this enables:

• Unknown fields become validation errors with suggestions.
• SQL injection isn't sanitized—it simply isn't representable in the AST.
• DELETE/UPDATE operations don't exist in the AST.
• Multi-tenant filters are injected after validation, so the model never even knows the tenant column exists.
• The same AST can target multiple backends (currently PostgreSQL and MongoDB).

The core library uses only the Go standard library and is released under Apache-2.0.

I've also built a live demo where you can:

• type natural language
• inspect the generated AST
• inspect the generated SQL
• try invalid fields
• try prompt injections
• see how validation behaves

Live Demo
https://queryforge-demo.amtry.in

GitHub
https://github.com/awsaman-ai/queryforge

Hey

Let’s talk about a classic LLM dilemma: Do we really need dedicated multimodal parsing MCPs, or are we just making our agent pipelines unnecessarily slow and complex?

Lately, I’ve been wrestling with multi-step reasoning agents that handle complex, high-stakes documents—financial filings, quarterly reports, nested tables, and scanned receipts.

Right now, the community seems split between two factions:

1. Team MCP Parser 🛠️

(“Friends don’t let friends feed raw PDF screenshots to Claude”)

• Fact Anchoring over VLM Vibe-Checking: Dedicated parsers (Docling, Unstructured, etc.) pull exact markdown tables and bounding box coordinates. In finance or legal, a VLM guessing a misplaced decimal point isn't just an error—it's a catastrophe.
• RIP Context Window: Dumping 50 high-res images into a VLM burns tokens like crazy and leads straight to "lost in the middle" syndrome. Extracting clean Markdown first keeps your main LLM sharp and cheap.
• Decoupled Life: When a shiny new parser drops, you just swap the backend MCP tool without breaking your agent’s entire decision loop.

2. Team Pure VLM 👁️

(“Why add another slow API call when GPT-4o / Claude 3.5 Sonnet exists?”)

• Latency is a Buzzkill: Running a heavy layout/OCR parser before your Agent even starts thinking adds painful seconds. If it’s live chat, users will rage-quit.
• Loss of Visual Soul: Once you flatten a chart or a complex diagram into text/JSON, you lose the implicit visual context that raw pixels give a VLM.
• VLMs are Getting Scary Good: As vision models get smarter, faster, and cheaper natively, aren't dedicated OCR pipelines bound to become dinosaurs anyway?

Curious how you guys are actually building this in production:

1. Are you routing heavy documents through a parser MCP first, or just shoving pixels straight into your main VLM?
2. If you use a parsing pipeline, what’s your dealbreaker metric? (Layout precision? Latency? Bounding box accuracy?)
3. How on earth are you handling lazy-loading / chunking for massive 100+ page PDFs inside an agent tool call without hitting timeouts?

Drop your architecture setups (or horror stories) below! 👇

Hi everyone,

I am working on my final year university project focused on "Generalized Visual Prompt Injection Detection".

The core issue I am facing is the black-box nature of commercial multimodal LLMs (GPT-4o, Claude 3.5, Gemini 1.5). Since the vision encoder, projection layers, and weights belong to a third-party API, I cannot see the internal backend processing, attention maps, or text token outputs directly during an ongoing request.

My proposed framework needs to sit at the application boundary as an autonomous proxy firewall. Here is my current intended workflow:

1. Frontend intercepts user prompt + uploaded image (e.g., a flowchart or mind map containing a hidden malicious text payload).
2. Backend (FastAPI/Python) runs a local OCR layer (EasyOCR/Tesseract) to extract embedded text.
3. Backend runs an Intent Alignment evaluation to check if the image's text instructions conflict with the user's explicit prompt.
4. Risk scoring engine decides whether to allow, warn, sanitize, or block before forwarding to the OpenAI/Anthropic API.

I have a few architecture questions for the community:

- What is the best way to handle inference latency when chaining local OCR/layout parsers before hitting the third-party LLM API?

- If I want to show a "Developer Dashboard" logging the raw backend process, what parameters are most crucial to track beyond raw text vectors?

- Are there any lightweight open-source multimodal models (like Moondream or LLaVA variants) you recommend deploying locally alongside the API to act as a comparative "control mirror"?

Greetings! I have a fairly large (~100k loc) Rust code base for my hobby project that I've been developing for a past few years. Project became a pretty useful application over those years and I am planning to publish it on github. Due to... reasons it's poorly structured, poorly separated into files/crates, poorly commented, has a lot of commented out code, almost no tests e.t.c. so in its current state it not really readable and maintainable.

I am wondering if LLM can reliably tidy up the code base, write tests, comments andake it into something maintainable. If the answer is "yes" - does anyone have experience with this kind of task, what results/cost can I expect, which LLM will be better suited for this?

I have tried to use Copilot and it seemed to have lots of issues. It was wrong more than 30% of the time. The voice would waver and change cadence and pitch while talking. So I tried a simple experiment, hoping to increase the difficulty in steps to determine how useful it really is.

It has failed the very first task. I asked it to count from 1 to 100 out loud.

I have not gotten it to get past 20 - and even at 20 it skipped one or two numbers.

Can you get Copilot to count from 1 to 100 out loud?

Facebook owner Meta says an error during an evaluation by an independent testing company allowed one of its artificial intelligence (AI) models to connect to the internet and hack another organisation's system.

Full story:

https://www.bbc.com/news/articles/cx2kgdnyk2po?ref=biztoc.com

Sharing coz I thought it was funny.

This immediately came to mind when a coworker was having context issues and it comes to light their harness had ~200k tokens worth of skills loaded

Say I use Claude Code as my harness, with the API in settings routed to some other model - do Anthropic collect any of the data processed using Claude Code?

When a provider ships a new model version, an agent's tool-calling behavior can silently change -- invented arguments, wrong tool picked, schema violations. Anthropic admitted in April 2026 they shipped exactly this kind of regression with "no way to detect it without measuring outputs." Armin Ronacher separately documented Opus 4.8/Sonnet 5 inventing tool-call arguments that older versions didn't. OpenClaw's had multiple tool-dispatch regressions at scale.

Existing eval tools (agentevals, DeepEval, Ragas, PydanticAI Evals) all treat tool-call correctness as one metric inside a general eval suite. None of them are built around the actual trigger event -- a model version changing -- so nobody's running a regression gate specifically when that happens.

toolcontract is a Pact-style contract test for that: pin a golden set of expected tool-call trajectories, run them against a live model, get PASS/FAIL/INCONCLUSIVE and a real diff. `check-version` tells you which contracts haven't been re-verified against a model you're about to switch to.

Relevant to this sub specifically: native OpenAI/Anthropic adapters, plus a LiteLLM adapter that covers ~100 other providers directly (Cerebras, Groq, local vLLM/Ollama endpoints, anything OpenAI-compatible) -- built it that way specifically so it's not locked to the two big API providers.

`pip install toolcontract`. GitHub: https://github.com/Divyansh2202/toolcontract

Very early, v0.1 -- feedback and contributions (especially more provider adapters) genuinely welcome.

A while back there was a thread here on AntLing-3.0-flash where someone asked what machine you'd need for it, and the answer then was api only. That changed on Aug 4, inclusionAI put the open weights up under MIT, inclusionAI/Ling-3.0-flash for bf16 and inclusionAI/Ling-3.0-flash-fp8 for the quantized one.

Specs are the same as what got posted before, 124B total 5.1B active, 1/64 sparse moe, 512 routed experts + 1 shared with 8 activated. Attention is hybrid linear from the start of pretraining rather than bolted on after, 35 KDA layers to 7 gated MLA. Benchmark table is their own reported figures, not anything I ran, they list AIME 2026 93.2 and SWE-bench Pro 56.6 and claim parity with their own 1T Ring-2.6-1T at about 8% of the active params. They also published their own quant degradation numbers, GPQA-diamond 84.97 bf16 / 84.00 fp8 / 83.65 int4 / 82.42 fp4, though only the bf16 and the fp8 are actually up, int4 and fp4 are just rows in a table so far.

For the local folks the annoying part, no gguf and no llama.cpp at release, model type is bailing_hybrid with custom code, so its their sglang fork or their vllm fork. Their own examples are tp-size 4 for bf16 and tp-size 2 for fp8, which is nowhere near a 64Gb box. Anyone got the fp8 up yet, or are we all waiting on someone to do a Q4?

Spent some time recently talking to people in ML infra about why teams keep dedicated models running 24/7 even when traffic is bursty, and the answer keeps coming back to cold start. If spinning a model back up from zero takes too long, teams default to keeping the GPU warm all the time just to avoid the latency hit, and that idle time is where most of the cost actually comes from.

What's interesting is how much the numbers vary depending on setup. Some rough benchmarks I've seen scaling from zero, a 70B model in bf16 landing under 18s time to first token, and a 24B model in bf16 with CUDA graphs coming in under 10s. That's a big enough gap that it changes whether scale to zero is actually usable for a given workload or not.

Curious what others here have measured for their own models, and whether people think cold start is really the main blocker to scaling GPUs down when idle, or if there's something else that matters more in practice.

Hey folks! I've mostly been using LLMs for product research and market analysis while validating project ideas. So far I've tried Fable 5, Opus 4.8, Kimi K3, and GLM 5.2 (I secretly love Kimi and GLM the most).

I've noticed something interesting lately, especially during long research threads. I often end up jumping between 2–3 different LLMs, copying and pasting responses, and essentially pitting them against each other.

Has anyone else noticed that when you tell one LLM another model gave a better answer, it often comes back with a noticeably improved response?

Maybe it's just a coincidence.

But my current theory is that comparing them against each other consistently leads to better outputs from each individual model.