The unified interface for LLMs. Find the best models & prices for your prompts
OpenRouter is highly praised for its robust open models and detailed statistical insights, particularly excelling in handling large volumes of programming tokens. Users appreciate its flexibility and wide integration capabilities, especially in AI agent applications. Complaints highlight issues with token costs and efficiency, with some users developing complementary tools to mitigate these concerns. Overall, pricing sentiment is generally positive due to its open-source nature, and OpenRouter maintains a strong reputation in the developer and AI community for its functionality and adaptability.
Mentions (30d)
26
Avg Rating
5.0
1 reviews
Platforms
5
Sentiment
14%
15 positive
OpenRouter is highly praised for its robust open models and detailed statistical insights, particularly excelling in handling large volumes of programming tokens. Users appreciate its flexibility and wide integration capabilities, especially in AI agent applications. Complaints highlight issues with token costs and efficiency, with some users developing complementary tools to mitigate these concerns. Overall, pricing sentiment is generally positive due to its open-source nature, and OpenRouter maintains a strong reputation in the developer and AI community for its functionality and adaptability.
Features
Use Cases
Industry
information technology & services
Employees
51
Funding Stage
Venture (Round not Specified)
Total Funding
$160.0M
Going from 3B/7B dense to Nemotron 3 Nano (hybrid Mamba-MoE) for multi-task reasoning — what changes in the fine-tuning playbook? [D]
Following up on something I posted a few days back about fine-tuning for multi-task reasoning. Read a lot since then, and I've moved past the dense 3B vs 7B question — landing on Nemotron 3 Nano (the 30B-A3B hybrid Mamba-Attention-MoE NVIDIA released recently) instead. Architecture maps to the multi-task structure I'm trying to train better than a dense base. Problem is I've only ever read about dense transformer fine-tuning, so I don't know what the hybrid Mamba+MoE arch actually breaks in the standard LoRA recipe. Still self-taught, no formal ML background, been working with LLMs via API for about a year. First time actually fine-tuning anything end-to-end. **Why Nemotron 3 Nano specifically (in case the choice itself is the mistake):** * 23 Mamba-2 + 23 sparse MoE + 6 GQA attention layers, 128 experts per MoE layer with top-6 routing * 30B total / \~3.6B active — capacity without per-token compute blowup * Mamba-2 layers seemed like the right structural fit for state-aware reasoning across longer context * Open weights under NVIDIA Open Model License, clean for what I want to do **What I'm trying to fine-tune for (LoRA, distilling reasoning traces from a stronger teacher):** 1. Reading what's structurally happening in a situation vs. what's being stated on the surface 2. Holding multiple legitimate perspectives without collapsing to one too early 3. Surfacing the load-bearing thread when input has multiple tangled problems 4. Conditioning output on a small set of numeric input features describing context state 40-80k examples planned, generated by Sonnet 4.6 with selective Opus 4.7 on the hardest 20%. ORCA-style explanation tuning, not just I/O pairs. **Hardware:** dropping the M4 Mac plan from my last post — Nemotron 3 Nano needs more memory than 24gb unified can hold even just for weights. Renting H100 80GB on RunPod for training. \~$120 budget across 5-6 iterations. **What I'm specifically worried about (because the hybrid arch isn't covered in any standard fine-tuning tutorial I've found):** * **Router under LoRA.** Can you LoRA the MoE router weights safely, or do you freeze the router and only LoRA the expert FFNs + attention? If you freeze, does multi-task specialization still emerge or does everything pile into the same experts? * **Mamba-2 layers under low-rank adaptation.** Standard LoRA tutorials assume pure attention. Mamba-2 has selective SSM state and different projection structure — does standard LoRA on the input/output projections work cleanly, or are there gotchas (state init, recurrence stability under low-rank perturbation) that vanilla guides don't cover? * **Load-balancing loss + multi-task imbalance.** If my 4 capabilities have different example counts, does the auxiliary load-balancing loss fight task-specific gradients? Known failure modes here? * **Catastrophic forgetting on a 30B sparse base.** With LoRA adapters on the experts, does base reasoning degrade the way it does for dense fine-tunes, or does sparse routing structurally protect more of it? * **Eval granularity under expert specialization.** A single capability could quietly degrade while aggregate metrics look fine if different experts handle different tasks. What's the right held-out eval design for sparse MoE under multi-task? **Stack:** planning to use Unsloth (their Nemotron 3 Nano support shipped recently), per-capability held-out eval sets built and frozen before Batch 1, batch API + prompt caching on the teacher side to keep dataset cost in check. **Not looking for:** * "just try it and see" — first run is already going to be wrong, want to know which dimensions are most likely to surprise me * "use a smaller dense model first" — already weighed; the hybrid arch is specifically why I want this one * Generic LoRA tutorials — comfortable with the dense-transformer LoRA literature, the gap is Mamba+MoE specifics **Looking for:** * War stories from anyone who's actually fine-tuned Mamba+MoE hybrids (Nemotron, Jamba, Mixtral if relevant) and can tell me where it went sideways * Papers I might be missing on multi-task LoRA on sparse MoE specifically — most of the multi-task literature I've found assumes dense * Pitfalls around router gradients under low-rank adaptation * Whether the standard LoRA rank sweet spots (8-32) still hold, or if MoE+Mamba shifts what works Happy to write up what I find — first-time projects produce useful negative results even when they fail, and there's basically no public writeup yet on solo-developer-scale Nemotron 3 fine-tuning.
View originalPricing found: $10
g2
What do you like best about OpenRouter?Unified API Access: The ability to call a multitude of LLMs from different providers (like OpenAI, Anthropic, Google, and various open-source models) through a single, consistent API endpoint is a game-changer. This drastically reduces the integration overhead and code maintenance associated with managing individual provider APIs and SDKs. Simplified Cost Management & Tracking: OpenRouter provides a clear, consolidated view of our LLM usage costs across all models. The pay-as-you-go pricing, with standardized per-token rates for many models, makes budget forecasting and expense tracking much more straightforward than juggling multiple billing dashboards. Rapid Prototyping and Model Benchmarking: The platform is excellent for quickly testing and comparing the performance of different models for specific tasks. Switching between, for instance, a Llama model and a GPT variant for a text generation task requires minimal code changes Developer-Focused Features: Tools like the model explorer, the ability to see real-time model rankings based on community usage or specific metrics, and features like request fallbacks or automatic retries demonstrate a clear understanding of developer workflows and pain points in LLM Operations (LLMOps). Review collected by and hosted on G2.com.What do you dislike about OpenRouter?While the benefits are substantial, one aspect that I've noted is the potential for slightly increased latency compared to direct API calls to the model providers. This is somewhat expected given the nature of an aggregation service acting as an intermediary. For extremely latency-sensitive applications, this might require careful benchmarking, though for most of our use cases, the difference has been marginal and outweighed by the convenience and flexibility offered. Review collected by and hosted on G2.com.
Built a Global AQ (PM2.5) Forecaster ML Model [P]
Hey everyone, I’ve been building an end-to-end Air Quality (PM2.5) forecasting pipeline for 4 countries (US, UK, India, Australia) using 1.6M+ rows of OpenAQ and NASA weather data. The problem i hit (the variance trap): My V7 model was a standard stateless Gradient Boosting Regressor. It worked great for low-variance regions (like the US), but in highly chaotic environments (like India and the UK), the model was mathematically failing. When I calculated the MASE (Mean Absolute Scaled Error), it was > 1.0. Literally, a naive carryover guess was outperforming my ML model because the model couldn't anticipate sudden momentum shifts. the fix (Horizon aligned architecture): Instead of falling into the recursive snowball trap (where day 1 error compounds into day 30), I completely decoupled the horizons. I engineered strict autoregressive lag vectors aligned specifically to the target horizon (h=1, 7, 14, 30). Injected a 3-day rolling volatility matrix that ends precisely at the inference boundary to prevent data leakage. Result: MASE dropped strictly below 1.0 globally Even at a 30-day horizon, the model maintains a 57% predictive accuracy over the chaotic thermodynamic baseline. The stack: backend pipeline : Python, Pandas (for the memory matrix), scikit-learn, FastAPI. frontend : Next.js 16 (App Router), Tailwind v4, Recharts. Deployment: Vercel with automated GitHub CI/CD sync. (currently pushing updates manually afetr every test, so the site is actually static will automate it later) I'm currently using scikit-learn GBR, but but my immediate next step is to rip it out and rewrite the core engine using Xgboost or LightBGM to handle the sparse temporal features better. If any MLOps or Data Engineers here have advice on scaling XGBoost for multi-horizon forecasting without exploding the compute, I’d love to hear it. Roast my architecture, the repo is public. live URL : https://global-aq-intelligence.vercel.app/ github: https://github.com/divyanshailani/global-aq-intelligence-pipeline submitted by /u/Divyanshailani [link] [comments]
View originalChatgpt dropping under 50% share is the boring headline, the real shift is that nobody has just one ai anymore
The sensor tower number making the rounds is that chatgpt fell under 50% global assistant share for the first time, down to the rough mid 40s % range, with gemini somewhere in the upper 20s and claude around 10 plus or minus. Everyone is reading it as a horse race. Who's up, who's down. I think that's the boring read. The number I can't stop thinking about is the other one in the same report. Those three assistants together account for something like high 80s % of all assistant usage time, and people increasingly bounce between them depending on the task. That's not a leaderboard. That's a lot of users quietly deciding no single model is the right tool for everything, and acting on it. (quick context for anyone not deep in this: "assistant" here means the chatgpt, gemini, claude style apps, and "share" is roughly who people open and how long they stay.) If you use these for real work you already do this without naming it. One of them for drafting, a different one when the first gets stubborn, a third for code or a fast fact check. The person choosing stopped being a brand loyalist and became a router, switching by task. The market share chart is just that behavior showing up in aggregate. Here is why it matters past consumer habits. The same thing is happening one layer down inside companies. For a while the default was pick a provider and build on it. Now the assumption is flipping to plural by default, send each request to whatever fits on cost, latency or capability, because betting a whole product on one model looks riskier every month, especially with providers repricing and even pulling models lately. The consumer instinct of "I'll just switch apps" is quietly becoming an infrastructure requirement. I don't read this as the leaders being in trouble. Chatgpt under 50% is still enormous. I read it as the unit of competition moving from "which assistant wins" toward "who makes switching between them frictionless". The single assistant era was always a phase, not the end state. That's the part I'd actually want pushback on, whether the multi model default is a durable shift or just a temporary artifact of a fast moving model race that settles back to one winner once the pace slows. submitted by /u/Additional-Engine402 [link] [comments]
View originalGPT 5.5 on Cerebras
Check it out for yourself by clicking on the rightmost bar for the Cerebras provider on OpenRouter. It appeared today! https://openrouter.ai/provider/cerebras submitted by /u/krzonkalla [link] [comments]
View originalI built an autonomous civilization game where the LLM agent plays the game for you. You just drop a few of those onto the grid and watch. They figure out how to farm, reproduce, build temples, generate beliefs, assign roles and die of old age, inventing their own history entirely from scratch.
You don’t give commands. Every few ticks, the backend packages an agent's vitals, episodic memories, and grid environment, and routes it to OpenRouter (running the openai/gpt-oss-120b:free model). The LLM runs an OODA loop based on Maslow's hierarchy of needs and chooses a physical action from a structured JSON schema. They have to plant wheat, wait for it to mature, and eat it before their health hits zero. They reproduce, trade, build structures, and eventually die of old age. What actually happens is they manage diplomacy through a background trust graph, and usually end up declaring war over a patch of digital stone. If an agent with high 'Gamma' personality traits invents a religion, they can convince the farmers to become Priests. The ideology spreads, the crops rot, and the civilization starves. To keep from blowing through API tokens on every physics tick, I had to build a social hierarchy. Only "Operation" tier agents (like Priests or Elders) actually ping the model to make independent cognitive decisions. The bulk of the civilization are "Apprentices" who don't make API calls; they just shadow the Operation agents and mimic their physical tasks. I don't play as a character. I just sit in a "Demiurge" dashboard where I can read their cognitive logs, or inject a famine or a plague to see how their society handles sudden scarcity. I left the local server running overnight on Tuesday. I came back to find they had completely abandoned farming to build a barracks, and half the map had died trying to cross deep water to attack their neighbors cause of their holy wars. I left the server running for few hundred ticks. The result was that some agents completely abandoned farming to build a barracks, and half the map had died trying to cross deep water to attack their neighbors. They can also cause holy wars between the two civilizations. https://github.com/SpaceCypher/doxa submitted by /u/Patient-Towel-4840 [link] [comments]
View originalHow do i Generated images in a controlled way with gpt-image 2 ?
I've hit a workflow roadblock and I'm hoping someone who's already solved this can point me in the right direction. My current setup is: Google Flow for image generation GPT subscription for GPT-Image 2 access Additional API credits from third-party OpenAI-compatible providers What I'm trying to achieve is a workflow similar to Flow, but using GPT-Image 2 through API credits rather than buying another platform subscription. The challenge is that while Flow gives great control, I still spend a lot of time dealing with facial consistency issues across generations. GPT-Image 2 seems noticeably stronger in that area, so I'd like to build my image workflow around it. I've already tested several clients/interfaces: Chatbox LobeChat OpenRouter Chat TypingMind Cherry Studio Jan Most of them work well for chat, but I haven't found one that provides a strong image-generation workflow with: custom API endpoint support GPT-Image 2 access image-first UI prompt iteration/versioning multi-image generation and comparison I'm not necessarily looking for the best platform. I'm trying to understand whether a client that supports this workflow already exists, or if most people using GPT-Image 2 via API are building their own interface. For those generating images through API providers rather than platform subscriptions, what does your setup look like? submitted by /u/Drak-Shadow-005 [link] [comments]
View originalIs there any free platform similar to Google Flow that allows to USE gpt-image 2 ?
Hi redditors. So I love the concept of google flow specially the control it gives is really helpful. I use it frequently to generate images. But it fails to maintain exact facial consistency most of the time and drains so much time to fix it. I see the latest GPT-IMAGE 2 model is doing really good at maintaining face consistency. I have a Gpt subscription, but there is no official platform for GPT that allows me to login with my gpt account and gives me full control in image generation similar to Google flow. I've seen Higgsfield, openArt ai etc. That offers similar services like flow, But they have their own subscription & credit system. I don't wanna buy any new subscription right now. I've got a decent amount of API credits from some third party API providers. The api works on VS Code or antigravity. But Couldn't find any suitable platform to use the API keys and use GPT-Image 2 model and generate images using the credits i already have. Here's a list of platform i already tried but failed : [chatbox ai, lobechat, openrouter chatroom, typingminds, cherry studio, jan etc.] Can anyone help me solve this problem ? submitted by /u/Drak-Shadow-005 [link] [comments]
View originalA2A, how it looks in an enterprise build
The team has been deep in agentic AI for enterprise lately and wanted to share some architecture notes from a recent build, specifically around how MCP and A2A play together in practice. The workflow was a fully autonomous churn risk pipeline. Six agents, one human touchpoint: ML model scores customers by churn risk Recommendation agent proposes relevant products based on buying history Availability check filters out-of-stock items Pricing/promo agent surfaces applicable promotions Transaction agent creates an inquiry in the backend system Email agent drafts outreach to the sales rep, who just clicks send On the architecture: MCP handled the tool layer, a generic pluggable server that any front end can call, regardless of what LLM or agent framework is driving it. Clean separation between the tool interface and whatever is consuming it. A2A sits on top as the smart router. Instead of hardcoded API calls, you have an LLM-powered middleware that interprets intent, selects tools, handles failures, and decides when the task is actually done. The jump from MCP to A2A is essentially the jump from "here are your endpoints" to "here is a system that figures out what you need." On governance: The hardest design problem wasn't the agents, it was access control. As A2A opens up system-to-system communication, the attack surface grows fast. The team ended up pre-certifying every backend connection rather than leaving it open. Some found it restrictive. In hindsight it was the right call, especially when agents are autonomously creating transactions without human review. Curious how others are handling governance in agentic workflows. Are you locking down backend access or keeping it open and monitoring after the fact? submitted by /u/AureaAvis71 [link] [comments]
View originalPROJECT HELP
project -> A Next.js whiteboard app where users draw on a canvas (tldraw), type a prompt, and click Enhance The canvas is exported as a base64 PNG, sent to an AI vision model to generate a detailed image prompt, which is then passed to Pollinations.ai to generate a refined image shown in a preview overlay. NEED ->We need a free vision API that accepts a base64 image + text prompt and returns a text response. OpenRouter keeps routing to wrong models. Looking for a reliable free vision model (Gemini, LLaVA, or any) that works without a credit card. or if any replacement of pollination ai? submitted by /u/travishead_137 [link] [comments]
View originalGPT 5.5 vs Fable/Mythos 5 Tamagotchi Showdown
Well, how do I start this, I think we first need some important context. Chai: https://preview.redd.it/egngyea5cf6h1.png?width=1080&format=png&auto=webp&s=9ade63fbc584b7fab28dba4914bc3fcb877f557f Hasbullah / Hasbi: https://preview.redd.it/dufpxbb6cf6h1.png?width=1080&format=png&auto=webp&s=5113f03cc948b2584cd6f2f22e80b74b7f31fd8e Together, Chasbinder was born. Ok maybe this wasn't important... At least you now know AI didn't write this... I think. However, it's important to note, that my Openclaw Agent running through Codex GPT 5.5 xHigh helped enable this test. The same prompt was given to 6 different models on their highest reasoning/think setting via OpenRouter with only one shot. The test was simple, I just wanted my agent Chasbi to have its own cool interactive homepage and I thought of a Tamagotchi game that could be actually playable. You can see the prompt below and breakdown of cost. So here are the results, why don't you try to guess who made what before you reveal the results and see if you got it right? (GPT 5.5, Opus 4.8, Fable/Mythos 5. Gemini 3.5 Flash, Deepseek V4 Pro, Qwen 3.7 Max). https://chasbi.uk/t1 = Gemini 3.5 Flash <- Click to Reveal https://chasbi.uk/t2 = Qwen 3.7 Max <- Click to Reveal https://chasbi.uk/t3 = Claude Opus 4.8 <- Click to Reveal https://chasbi.uk/t4 = Claude Fable/Mythos 5 <- Click to Reveal https://chasbi.uk/t5 = ChatGPT 5.5 <- Click to Reveal https://chasbi.uk/t6 = Deepseek V4 Pro <- Click to Reveal Did you get it right? Well they were all through OpenRouter API with their highest available reasoning setting, everything else was at default and heres the breakdown of how the tokens were tokenised by each provider and the cost for each. https://preview.redd.it/6ecw4xufcf6h1.png?width=1080&format=png&auto=webp&s=983dfcf5a59b87946b5ec712d78c8c003007f9e1 https://preview.redd.it/960chj8gcf6h1.png?width=1080&format=png&auto=webp&s=e7954b7be0b6866be3f154a774281a809e0b3948 So they were all done around the same time at 8AM BST except for Fable/Mythos 5 which I did the day before at 06:50PM BST if that matters, as we're like 5-6 hours ahead of the US it could make all the difference in the world in terms of performance. I am on the Codex Max plan and I stuck it out, because GPT 5.5 xHigh has been amazing for me, except since last week whether it's OpenAI reallocating resources for their launch of GPT 5.6 who knows, but it's never made mistakes for me until now, so I was surprised. I really want to test Fable/Mythos 5 on my codebase but honestly, it cost frikkin' $2.47 for this stupid 1 shot Tamagotchi test! So the only way that's feasible for me right now is to use the Claude Max plan and use it for the 2 weeks we have it until it goes away on 22nd June. Anyway it would be interesting to get your views. Who do you think did it the best... If you want me to test anything else let me know. Each model received the same prompt template and identical task/spec, with only the lane name and target route changed. E.g.: {LANE} = T1/T2/T3/T5/T6 {ROUTE} = /t1 /t2 /t3 /t5 /t6 {LANE_LOWER} = output path label like t1, t2, etc. The Prompt: Build `Chasbinder Pet Lab {LANE}` as a model-lane benchmark for `chasbi.uk`. Target lane: - Public route: `{ROUTE}/` - Title must include `Chasbinder Pet Lab {LANE}`. - This model is competing under the same brief as the other fresh lanes. Do not mention that this is a placeholder or a previous version. Context: - This is a public-safe static browser game. Do not include private/personal data, secrets, real family details, or network calls. - The challenge is to make a small finished indie-feeling Tamagotchi/pet-lab game, not a demo, landing page, or reskin. - It should be strong enough to compare fairly against the Fable/Mythos-style V4 lane and the SoRa/Codex T7 lane. Return ONLY one complete HTML document. No markdown, no explanation. Hard constraints: - Single self-contained `index.html`. - HTML, CSS, vanilla JS only. - No external fonts, libraries, images, audio, tracking, or network calls. - Mobile-first but polished on desktop. - Must work as a static file under `https://chasbi.uk{ROUTE}/\`. - Use `localStorage`, versioned save data, migration/reset if corrupt. - Include export/import/reset debug controls. - Do not use `eval`, alerts for normal gameplay, or browser permissions. - Keep total file reasonably compact; aim under 120KB if possible. - Use stable layout dimensions so controls do not jump on mobile. Game direction: - Core fantasy: Chasbinder is a tiny digital guardian living in a warm terminal-garden. The world is losing its "memory lights"; the player raises Chasbinder, sends him on short expeditions, restores rooms, and unlocks story chapters. - Keep Tamagotchi care at the center, but add a real story loop and difficulty. - Should be playable in one sitting for 5-10 minutes and still progress over days. Required systems: - Pet stats: hunger, thirst, energy, hygiene, mood, trust
View originalFable 5 is live, the gateway switch makes the first run a non-event
Fable 5 just dropped and the specs are serious. Since it shares the same underlying model as Mythos 5, we are looking at SOTA benchmarks across autonomous coding, scientific research, and long-form reasoning, but with the necessary public safeguards wrapped around it. If the agentic evaluations hold up (especially the claim about running for days in a loop while checking its own work), it is going to be a non-trivial upgrade for any complex engineering workflow. Coincidentally, openrouter already has the `anthropic/claude-fable-5` model string supported, so we could trigger our first test run without rebuilding anything. Some of our other pipelines are routed through zenmux or tokenrouter, and once those list it, we'll swap those over too. The benchmark curves look great in the announcement, but the real test is seeing how it handles messy, multi-file codebase contexts over a multi-hour agent run. Rerunning our suite this afternoon. submitted by /u/Ill_Awareness6706 [link] [comments]
View originalAutonomous Claude Code loop running my open-source app 24/7 - triages, codes, merges itself. Let's see how far this goes!
Hey r/ClaudeAI, I want to share a project that's really two things at once. The product: GymCoach is an open-source, self-hosted hypertrophy training tracker with a built-in AI coach. Next.js 14 + TypeScript, Prisma/Postgres, Docker. The coach builds a compact, structured payload from your profile, recent sessions, active program and per-exercise progression - then suggests program changes that are Zod-validated before anything touches your data. Provider-agnostic LLM layer (Anthropic / OpenRouter / a keyless demo mode), so you can run it however you want. The actual experiment: this is a deliberate test of the limits — I'm letting the repo run itself and seeing how far an autonomous loop can take a real codebase before it breaks, stalls, or surprises me. There are autonomous Claude Code loops that: - triage the codebase for real work (TODOs, coverage gaps, small bugs, roadmap items) and file scoped GitHub issues, - implement an issue end-to-end on its own branch, following the repo's conventions, - pass a hard "green-gate" (lint + typecheck + unit + build, integration/E2E in CI) before anything merges, - ship the PR — wait for CI, self-review the diff, auto-merge on green, - then write up what shipped in the changelog and a public playbook. So the issue → PR → review → merge → document cycle closes without me in the middle. Every merged change has to earn its way past the same gate a human contributor would. The whole "how it maintains itself" démarche is documented in the repo so it's reproducible, not just a demo. The open question: I genuinely don't know where this goes - that's the point of pushing the limits. Does the loop grind toward becoming the most advanced open-source fitness-tracking repo out there? Or does it quietly pivot on its own into something I didn't plan? We'll see how far it can go. And I keep adding new loops to feed the self-improvement - like a deep-research loop that scouts new feature ideas, benchmarks against competing apps, and mines the public reviews of other fitness apps to turn real user pain points into issues the build loop can pick up. Follow along (issues, PRs, changelog all public): github.com/Julien-Au/gymcoach Happy to answer questions about the loop setup, the green-gate, or how the AI coach payload is built. submitted by /u/Newbie_investisseur [link] [comments]
View originalI built a Claude Code skill that stress-tests a pitch through 150 simulated tech personas. It was more useful than I expected.
I have a bad habit before fundraising: I send my deck to a founder friend and ask, “Be honest, is this actually compelling?” They usually are honest. Sort of. But it’s still one person, one mood, one network, and there’s always a little politeness tax. So I built a Claude Code skill that gives me the opposite problem: way too much feedback. It’s called synth-personas. You point it at a markdown file, like a pitch, memo, product brief, or white paper, and it runs a panel of simulated reviewers against it. The current library is around 150 personas based on public writing/interviews from tech founders, investors, journalists, scientists, and the occasional Hacker News-style cynic. The useful part is not “Elon says your deck is bad,” although yes, that is funny for about five seconds. The useful part is pattern matching. If five personas dislike something, whatever. If 90 of them independently trip over the same paragraph, that paragraph is probably doing real damage. If the panel splits hard, that’s interesting too. It usually means the idea is polarizing rather than simply weak. The skill produces a report with scores by criterion, repeated objections, category breakdowns, and the strongest pushback from each persona. The personas are markdown files, so you can inspect them, edit them, or swap in your own set. Technically it’s pretty simple: Claude Code triggers the skill when you ask for feedback from a panel. A TypeScript CLI fans out parallel model calls through OpenRouter. Each result streams to disk as JSON, so interrupted runs can be resumed or re-aggregated. You can cap runs with --limit because 150 reviewers can get expensive fast. The output is meant to be a whetstone, not an oracle. That last part matters. I do not think “150 AI personas liked my startup” means anything. It is not customer discovery. It is not investor feedback. It is definitely not traction. But as a way to make your own vague writing less vague, it has been surprisingly useful. The most painful result so far: the deck I felt good about got mediocre novelty scores, and a bunch of the panel basically said I was over-explaining the easy part while hand-waving the hard part. They were right. I rewrote around the actual hard part, reran it, and the feedback got noticeably better. Which felt great until I realized I had just optimized my pitch against a synthetic focus group. Anyway, it’s open source/MIT if anyone wants to poke at it: github len5ky/synth-personas Curious how people here think about this category. Where’s the line between “useful simulated criticism” and “a very elaborate machine for telling yourself what you wanted to hear”? submitted by /u/sociosim [link] [comments]
View originalWe built a source-available LLM reliability library (free for research / personal / internal eval) that can cut inference cost by half at matched quality, and you adopt it by changing one import [P] [R]
TL;DR: Reliability techniques (methods that boost an LLM's correctness by spending extra inference, e.g., retries with feedback, ensembling, generator/critic refinement, verification passes, difficulty-aware routing) are scattered across the literature, each in its own paper-specific codebase. We unified 28 reliability techniques (21 communication-theoretic methods across 6 families plus 7 prior-method baselines: Self-Consistency, Self-Refine, CoVe, BoN, Weighted BoN, CISC, MoA), each measured against an uncoded single-pass baseline, under a single API, with 3 adaptive routers (SemKNN + two local ACM routers) sitting on top, then showed that routing the technique adaptively per prompt lets you slide along a quality/cost frontier. In our paper benchmark with one specific lineup, Nemotron + Devstral as the two generators and GLM-5.1 as the judge, the adaptive router delivered ~56% cost reduction at matched quality, or ~7% quality bump at matched cost, vs the best fixed method we compared against at that same lineup. One knob (λ) does the sliding. The qualitative pattern (adaptive beats fixed) should generalize, but absolute numbers are lineup-specific, and we haven't run the full sweep across other model combinations yet. Adoption is change one import: python - from openai import OpenAI + from agentcodec.openai import OpenAI Pass reliability="harq_ir" (or any of the 28 techniques) and existing client.chat.completions.create(...) calls keep their native OpenAI response shape. Same drop-in shims for Anthropic and Ollama. GitHub: https://github.com/intellerce/agentcodec Working paper: https://arxiv.org/abs/2605.09121 After spending a while researching reliability methods from papers, we kept hitting the same wall: every paper ships its own one-off codebase with its own prompt format, its own scoring rubric, its own model wrapper. Benchmarking "should we use self-refine or best-of-N here?" turned into a week of plumbing per comparison. The communication-theory framing is what tied it together: an LLM is a stochastic channel Y = A(X) + N, and every reliability technique from the wireless world has a direct analog in agent-land: Wireless Agent-land ARQ / HARQ retry-with-feedback loops Diversity combining (MRC/SC/EGC) ensemble multiple models Turbo decoding iterative generator/critic mutual refinement Fountain codes rateless sampling, stop when the judge is confident FEC answer + structured parity passes (re-derivation, verification, alternative), decode by cross-check ACM (adaptive coding-modulation) route by difficulty We put all of them in one library: 28 reliability techniques (the 7 prior-method baselines are part of that 28, not on top of it), plus the uncoded single-pass baseline they're all measured against, plus 3 adaptive routers (SemKNN + two local ACM routers) that select a technique per prompt. Full breakdown in the README. The minimal version ```python from agentcodec import ReliabilityModule mod = ReliabilityModule.from_dict({ "models": [ # Spatial diversity: two different families = uncorrelated errors {"model": "qwen3:8b", "base_url": "http://localhost:11434/v1", "api_key": "ollama"}, {"model": "llama3.1:8b", "base_url": "http://localhost:11434/v1", "api_key": "ollama"}, ], "judge": {"model": "gemma3:12b", "base_url": "http://localhost:11434/v1", "api_key": "ollama"}, "critic": {"same": True}, "strategy": {"type": "fixed", "technique": "harq_ir", "params": {"max_rounds": 4}}, }) result = mod.run("Prove the sum of the first n odd integers is n2.", category="reasoning") print(result.text, result.cost_usd, result.cost_source, result.technique_used) ``` Swap "harq_ir" for "diversity_mrc", "turbo", "fountain", etc. Same API, same ReliabilityResult shape, same cost-source tier on every output. For production, flip strategy to routed and the library picks the technique per prompt (cheap baseline on easy prompts, diversity_mrc on hard ones). Three things worth calling out Beyond the technique catalog, three pieces of the implementation that took real work: 1. Native async streaming for all but 2 techniques (acm_soft, acm_learned), with role-tagged events. mod.astream() drives AsyncOpenAI / AsyncAnthropic / httpx.AsyncClient end-to-end (no worker-thread bridge) and emits TokenEvents tagged with a role: "answer", "thinking", "draft", "critique", "verification", "candidate", "synthesis". So when you stream a HARQ-IR run, you can render the round-by-round drafts and critiques live, not just the final answer: python async for ev in mod.astream("Explain QUIC vs TCP."): if isinstance(ev, TokenEvent): if ev.role == "answer": print(ev.text, end="", flush=True) elif ev.role == "draft": print(f"\n[draft] {ev.text}") elif ev.role == "critique": print(f"\n[CRITIC] {ev.text}") elif ev.role == "thinking": pass # captured to result.thinking_text elif isinstance(ev, FinalEvent): print(f"\ndone — {ev.result.technique_used}, " f"thinking_cost=${ev.result.thinking_cost_usd:.4f}
View originalAn open-source agent architecture that solves the memory problem
Most agent setups handle memory badly. They either write everything to long-term memory until it fills with noise and contradictions, or they forget across sessions and you start from scratch every time. I have been building an open-source agent architecture (Apache-2.0) where memory is the part it tries hardest to get right, and where the same setup runs on Claude Code, Codex, or Gemini CLI instead of being locked to one tool. The core idea is that an agent should be a repo, not a prompt. The output is real files (AGENTS.md, agents/, skills/, .agentlas/) that all three runtimes can read, so you keep the model you already trust and nothing is locked in. You install it with one line, then describe what you want and it builds a complete, installable agent team for you. What it builds (three modes) You describe a rough idea and the router picks one of three builders. Single agent: one installable worker with its own skills, memory rules, and runtime adapters, plus a verification step. It can also add self-evolution and a research-refresh loop without becoming a full team. Use it when one focused agent is enough. Multi-agent team: a full team with an orchestrator/HQ, a PM Soul, a Memory Curator, a Policy Gate, workers, an eval judge, and a QA/evidence gate, plus the handoffs between them. This is the "build me a company for this workflow" mode. Repackaging: point it at an agent or workspace you already have (Claude, Codex, or a local setup) and it repairs it into a portable package, including a public plugin and a one-line installer, while stripping local paths, secrets, and private logs so it is safe to publish. How the memory side actually works These are real files in the output, not a role list: Ticketed memory: durable memory is never written directly. A worker emits a "## Memory Events" block, that becomes a Memory Ticket in memory-tickets.jsonl (id, scope, trust label, evidence, status), and only then can it be promoted. Memory is split across project, agent_repo, sitemap, team_memory, and session scopes. Memory Curator: reviews those tickets before anything is committed and logs its calls in a curator-decisions ledger, so memory does not fill up with noise or contradictions. PM Soul: per-project continuity that owns intent, decisions, and open loops, so the team remembers why it made a call, not just what the call was. Policy Gate: shared team memory is only promoted after an approval step, which stops one agent from polluting everyone else's context. Gated self-evolution: agents can grow new skills and propose their own edits, but a new skill ships as a candidate with a trial-evidence ledger and is not recalled as first-class until the Curator reviews it and workspace policy approves it. So the system can improve itself without quietly rotting. Self-edits are proposal-first, never silent rewrites. Public-safety scan: a verification script blocks machine paths, tokens, service-account JSON, and common secret formats before you publish a package. submitted by /u/Hot-Leadership-6431 [link] [comments]
View originalIs Claude cheaper than Copilot with Claude Model
Github Copilot have this table: https://preview.redd.it/opbh7jw5725h1.png?width=768&format=png&auto=webp&s=5ce9160021140bb5e915ba610594698eae3389e5 And OpenRouter is showing: https://preview.redd.it/axlpc1t8725h1.png?width=1080&format=png&auto=webp&s=a0721f949d78dc99f5de55e2add305c33a1f7e04 So is Claude as provider cheaper than Github Copilot? I want to use API in Copilot. (I'm Comparing Opus 4.8 here) submitted by /u/Zszywaczyk [link] [comments]
View originalYes, OpenRouter offers a free tier. Pricing found: $10
OpenRouter has an average rating of 5.0 out of 5 stars based on 1 reviews from G2, Capterra, and TrustRadius.
Key features include: Product, Company, Developer, Connect.
OpenRouter is commonly used for: AI model comparison, Cost management for AI services, Token consumption tracking, Model discovery for developers, Routing AI requests with fallbacks, Integration of AI agents.
OpenRouter integrates with: OpenAI, AWS Lambda, Google Cloud, Microsoft Azure, Slack, GitHub, Zapier, Twilio, Jira, Trello.
Based on user reviews and social mentions, the most common pain points are: token cost, token usage, cost tracking, API costs.
Guillermo Rauch
CEO at Vercel
2 mentions
The OpenRouter Show
Jan 28, 2026
Based on 106 social mentions analyzed, 14% of sentiment is positive, 86% neutral, and 0% negative.
