Alternatives
Products that do what MarcoFLY Framework AI does
Bringing epistemic discipline to everyday AI
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Steiner is a series of reasoning models trained on synthetic data using reinforcement learning. These models can explore multiple reasoning paths in an autoregressive manner during inference and autonomously verify or backtrack when necessary, enabling a linear traversal of the implicit search tree. Blog: https://medium.com/@peakji/a-small-step-towards-reproducing-... Hugging Face: https://huggingface.co/collections/peakji/steiner-preview-67...
2024 · medium.com
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Agentic problem solving in its current state is very brittle. I fell in love with it, but it creates as many problems as it solves. I'm Ben Cochran, I spent 20+ years in the trenches with full-stack Engineering, DevOps, high performance computing & ML with stints at NVIDIA, AMD and various other organizations most recently as a Distinguished Engineer. For agents to work reliably you either need massive parameter counts or massive context windows to keep the solution spaces workable. Most people are brute forcing reliability with bigger models and longer prompts. What if I made the problem…
May 2026 · github.com
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I think agent-first chat interfaces will be a primary software modality and busy dashboard/UI will go away. I’m not sure who exactly wins it, but I want my knowledge to grow/go with me. A lot of the “knowledge” ie research, analysis, reasoning will be done by agents as the primary user. Our current notes tools & tasks management systems were built for humans… I don’t care what the 17th thing on my bug backlog is. I want to conduct agents that can execute for me and do great work. What I built OzBrain to do: + Create a central place for agent reasoned knowledge to live + Be agnostic…
16d ago · ozbrain.com
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Oct 2025 · agentml.dev
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A unified control plane for Magento with Claude Code web
Jun 2026 · storeframe.io
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We implemented Stanford's Agentic Context Engineering paper which shows agents can improve their performance just by evolving their own context. How it works: Agents execute tasks, reflect on what worked/failed, and curate a "playbook" of strategies. All from execution feedback - no training data needed. Happy to answer questions about the implementation or the research!
Oct 2025 · github.com
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2025 · github.com
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GitHub: https://github.com/ClioAI/kw-sdk Most AI agent frameworks target code. Write code, run tests, fix errors, repeat. That works because code has a natural verification signal. It works or it doesn't. This SDK treats knowledge work like an engineering problem: Task → Brief → Rubric (hidden from executor) → Work → Verify → Fail? → Retry → Pass → Submit The orchestrator coordinates subagents, web search, code execution, and file I/O. then checks its own work against criteria it can't game (the rubric is generated in a separate call and the executor never sees it…
Feb 2026 · github.com
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Dynamiq is an orchestration framework for agentic AI and LLM applications
2024 · github.com
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TLDR: A small, vendor-agnostic inference loop that turns token logprobs/perplexity/entropy into an extra pass and reasoning for LLMs. - Captures logprobs/top-k during generation, computes perplexity and token-level entropy. - Triggers at most one refine when simple thresholds fire; passes a compact “uncertainty report” (uncertain tokens + top-k alts + local context) back to the model. - In our tests on technical Q&A / math / code, a small model recovered much of “reasoning” quality at ~⅓ the cost while refining ~⅓ of outputs. I kept seeing “reasoning” models behave…
2025 · github.com
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2024 · github.com
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I'm one of the creators of The Edge Agent (TEA). We built this because we needed a way to deploy agents that was verifiable and robust enough for production/edge cases, moving away from loose scripts. The architecture aims to solve critical gaps in deterministic orchestration identified by *Prof. Claudionor Coelho Jr. (Stanford alum, ML/DL Faculty at Santa Clara Univ., and Senior Fellow for AI at Majestic Labs)* during our work on the Kiroku project. *Key Technical Features:* * *Neurosymbolic Native:* We integrated Prolog to logically validate LLM outputs. This combines neural…
Jan 2026 · fabceolin.github.io
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Yesterday I built something that probably shouldn’t exist yet. In 9 hours, I created a cognitive architecture demonstrating emergent reasoning. It follows a 5-step loop: Plan → Reason → Act → Reflect → Respond. Adding a WebSearchTool to test extensibility, the agent initially failed its first search, reflected on poor results, adapted its query, and then succeeded. This behavior wasn’t programmed; it emerged naturally from the architecture. Five hours later, I integrated a FileManagerTool — it worked on the first try. Like code compiling first time, except this was intelligence composing…
2025 · github.com
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Jan 2026 · railly.dev
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WFGY introduces a PDF-based semantic protocol designed to correct projection collapse, contradiction loops, and ambiguous inference chains in LLMs. No retraining. No system calls. When parsed, the logic patterns alter reasoning trajectories directly. Prompt evaluation benchmarks show: ‣ +42.1% reasoning success ‣ +22.4% semantic alignment ‣ 3.6× stability in interpretive tasks The repo contains formal theory, prompt suites, and reproducible results. Zero dependencies. Fully open-source. Feedback from those working in alignment, interpretability, and logic-based scaffolding would be…
2025 · github.com
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HN, first things first: one year ago you make me believe in my opensource AI project, and I'm forever grateful[1]. I am back with Beam - a technique to use diverse LLMs to generate responses, and Merge them - all within a snappy UX. I am no researcher, so you'll find a dark-mode blog, and not a light-mode PDF on arxiv :) Blog, open code, and live hosted demo, all published. You can use Beam early on in a chat, where looking at more options is key to be more confident in the answer, but also when no answer if perfect, but fusing many together will work well. Take a look and let me know what…
2024 · big-agi.com
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