Alternatives
Products that do what DeepSeek-V3.1-Terminus does
A refined agentic model for developers
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2025 · youtube.com
- 14GA
Hello! Introducing geniusrise, an agent framework and component ecosystem for building AI agent networks that are as flexible as your team. landing page: https://geniusrise.ai (fancy but useless) docs: https://docs.geniusrise.ai (please check this out) github: https://github.com/geniusrise (for dear devs) ## Thought process Since the ChatGPT disruption, I've been pondering on what the tooling layer is going to look like for building LLM-interfacing agents. Saw a plethora of tools coming out as we witness here every week. I'd broadly categorize them into the…
2023 · github.com
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There is an adversarial relationship between developers and big model labs. Model labs charged developers higher API prices to subsidize their own agent harness offerings. Think Anthropic charging 5x higher Claude API prices to subsidize consumer subscriptions. So Cursor in a way was subsidizing their own direct competitor. DeepSeek V4 Flash totally inverted this relationship. Now you have a model that beats even Sonnet in some benchmarks and is totally opensourced. Now inference providers are racing to the bottom to optimize and give cheaper hosting. Every player with a non-SOTA is now…
Jun 2026 · rtrvr.ai
- 17D0
We used our platform to fine-tune a tiny text-to-SQL model using distillation from DeepSeek V3. Repo has instructions for how to replicate this. This is definitely not the best-performing model like this out there! But I found it surprising we were able to get to this much out of it: stone's throw away from a teacher 1000x the size! We also ran the same thing using the 4B Qwen and matched the teacher accuracy, though here the difference is merely 100x :) I find this pretty cool - obviously our distilled models can only do this one task and don't generalize, but that's often exactly what you…
Jan 2026 · github.com
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We’re excited to share ML-Dev-Bench, a new open-source benchmark that tests AI agents on real-world ML development tasks. Unlike typical coding challenges or Kaggle-style competitions, our benchmark simulates end-to-end ML workflows including: - Dataset handling and preprocessing - Debugging model and code failures - Implementing new model architectures - Fine-tuning and improving existing models With 30 diverse tasks, ML-Dev-Bench evaluates agents across critical stages of ML development. To complement this, we built Calipers, a framework that provides systematic performance evaluation and…
2025 · github.com
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Hi everyone! We just launched Depth AI - a tool that helps you onboard to large and messy codebases. Unlike most dev tools that help in codegen and building smaller apps, this one mainly aims at understanding large repos better - so we have focussed a lot of code search quality. We also launched the first version on product hunt https://www.producthunt.com/posts/depth-ai. Do check us out. Would love to hear feedback here and discuss more how our approach to code search is different.
2024
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Hi HN, Martin, Nils, and Jannes here. We are building Legit, an open source version control and collaboration layer for AI agents and AI native applications. You can find the repo here https://github.com/Legit-Control/monorepo and the website here https://legitcontrol.com Over the last years, we worked on multiple developer tools and AI driven products. As soon as we started letting agents modify real files and business critical data, one problem kept showing up. We could not reliably answer what changed, why it changed, or how to safely undo it. Today, most AI…
Jan 2026
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What relai-sdk is an open-source toolkit for making AI agents reliable via a complete learning loop: simulate → evaluate → optimize. Why Agent runs are stochastic; tool-calls fail; hard to reproduce, measure, and fix at scale. It’s also hard to align behavior with goals across output quality/format, cost, and latency. We need a loop that integrates user feedback and LLM evaluators directly into the agent code (prompts, configs, models, graphs) without overfitting. How - Simulation: LLM personas, mocked MCP servers/tools, synthetic data; can condition on real traces - Evaluation:…
Oct 2025 · github.com
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hello everyone, my first post! AA here, founder of ⌘ Langbase.com — we are a developer platform for building and scaling serverless AI memory agents. I know surveys can be boring, but this one’s different—it’s interactive! That's very much intentional. My team and I have been up for the last 21 hours putting together this report. This was a looot of work, so I hope y'all like it. Introducing … State of AI Agents 2024 report On Langbase, we processed 184 billion tokens and handled 786 million AI agent runs from 36K developers. From all that data plus insights from 3.4K builders who filled out…
2024 · langbase.com
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Hey HN, In the months since we initially released Burr (https://news.ycombinator.com/item?id=39917364), we have been hard at work. We wanted to share some of the most exciting changes we’ve made to build Burr out as a full-stack development framework for AI agents. In case you don’t recall, Burr is an open-source python library that makes it easier to build and debug GenAI applications & agents by representing them as graphs of simple python objects/functions. Burr only abstracts away system-level concerns (state persistence, debugging, observability), and does not…
2024 · burr.dagworks.io
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I've built an open-source implementation of Google DeepMind's AlphaEvolve system called OpenEvolve. It's an evolutionary coding agent that uses LLMs to discover and optimize algorithms through iterative evolution. Try it out: https://github.com/codelion/openevolve What is this? OpenEvolve evolves entire codebases (not just single functions) by leveraging an ensemble of LLMs combined with automated evaluation. It follows the evolutionary approach described in the AlphaEvolve paper but is fully open source and configurable. I built this because I wanted to experiment with…
2025
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