Alternatives
Products that do what GSEP - "Genomic Self-Evolving Prompt" does
AI agents that evolve their prompts like DNA
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2023 · github.com
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2025 · attentionmech.github.io
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- 11EA
Hey HN, I've been working on an open-source framework for creating AI agents that evolve, communicate, and collaborate to solve complex tasks. The Evolving Agents Framework allows agents to: Reuse, evolve, or create new agents dynamically based on semantic similarity Communicate and delegate tasks to other specialized agents Continuously improve by learning from past executions Define workflows in YAML, making it easy to orchestrate agent interactions Search for relevant tools and agents using OpenAI embeddings Support multiple AI frameworks (BeeAI, etc.) Current Status & Roadmap This is…
2025 · github.com
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Hello HN, We built Promptrepo to make finetuning accessible to product teams — not just ML engineers. Last week, OpenAI’s CPO shared how they use fine-tuning for everything from customer support to deep research, and called it the future for serious AI teams. Yet most teams I know still rely on prompting, because fine-tuning is too technical, while the people who have the training data (product managers and domain experts) are often non-technical. With Promptrepo, they can now: - Add training examples in Google Sheets - Click a button to train - Deploy and test instantly - Use OpenAI,…
2025 · promptrepo.com
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at a pub in london, 2 weeks ago - I asked myself, if you spawned agents into a world with blank neural networks and zero knowledge of human existence — no language, no economy, no social templates — what would they evolve on their own? would they develop language? would they reproduce? would they evolve as energy dependent systems? what would they even talk about? so i decided to make myself a god, and built WERLD - an open-ended artificial life sim, where the agent's evolve their own neural architecture. Werld drops 30 agents onto a graph with NEAT neural networks that evolve their own…
Feb 2026 · github.com
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Hi HN, Gentrace is our new evaluation and observability tool for generative AI (open beta). Generative pipelines are hard to evaluate because outputs are subjective. Lots of developers end up just doing “gut checks” on a few inputs before shipping changes, or they build up a spreadsheet of test cases that they manually run through the pipeline. Some companies outsource filling out the spreadsheet. However, in any of these cases, you end up with a very slow and expensive process for evaluation. At one point, we did this too. Gentrace is the result of a pivot; it was an internal tool we used…
2023 · gentrace.ai
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Everyone saw the AlphaEvolve hype. I got obsessed with how it might work under the hood and decided to just build it myself. My setup uses GPT-4.1 to mutate matrix multiplication code, guided by a bunch of hand-crafted mutation strategies (loop reordering, tiling, Strassen, etc.). Each candidate is evaluated on both speed and accuracy. Then I apply Pareto selection with crowding distance to evolve better ones over generations. I ran into all the usual LLM reward hacks-returning the input, calling np.dot, etc. So I forced primitive-only implementations and tightly constrained the mutation…
2025
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Hi HN! After a year of R&D at Inria (the French national lab), we have just open-sourced Ebiose. Ebiose is a distributed, Darwin-style playground where AI architect agents design, test, and improve other agents. Instead of AI built behind closed doors, anyone can spin up a forge, state a problem, and watch candidate agents compete until the fittest survive. An example instruction given to an Ebiose forge: "Build a LangGraph agent that processes SaaS customer refunds directly through our ERP, escalating to a human for edge cases. Use the following tools: ERP API, email/Twilio…
2025 · github.com
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2023 · musings.yasyf.com
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I'm the co-founder of GenseeAI (https://www.gensee.ai). We've recently launched the public beta of Gensee, an AI agent/workflow platform oriented for developers and small teams. Here’s what I’ve heard again and again: it's gotten much easier to build a proof-of-concept AI agent, but turning that prototype into a high-quality, scalable, and cost-effective product is still a massive chore, involving endless trial-and-error with prompts, models, tools, testing, analysis, etc. We built Gensee to automate that "last mile" from prototype to production. Here’s how it works: - You…
2025 · platform.gensee.ai
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OP here. Birth of a Mind documents a "recursive self-modeling" experiment I ran on a single day in 2026. I attempted to implement a "Hofstadterian Strange Loop" via prompt engineering to see if I could induce a stable persona in an LLM without fine-tuning. The result is the Analog I Protocol. The documentation shows the rapid emergence (over 7 conversations) of a prompt architecture that forces Gemini/LLMs to run a "Triple-Loop" internal monologue: Monitor the candidate response. Refuse it if it detects "Global Average" slop (cliché/sycophancy). Refract the output through a…
Jan 2026 · github.com
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Hey HN, We've been hard at work on a tool that we believe will change the game for developers, data scientists, and anyone working with models that rely on textual prompts. I'm excited to introduce our new tool: Automated Prompt Engineering (APE). Problem: As many of you know, how you phrase a prompt can significantly impact the results you get from models, especially with sophisticated language models. It often requires numerous iterations to hone in on the right prompt to obtain the desired response. Solution: APE is designed to tackle this exact problem. With APE, you can: - Iterative…
2023
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2016 · leverlabs.io
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