Alternatives
Products that do what I open-sourced a $1M engine for closing loops in embedding space does
Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns - No internal loopback or self-calibration - No modular thought units - Weak…
- 1IS
Everything that would be here is in the README. I hope this gets big, it has tons of potential.
2013 · github.com
- 2AT
We kept shipping “simple” LLM features that were fluent-but-wrong. After too many postmortems we wrote down the failure patterns and added a small reasoning layer in front of the model. It’s model-agnostic, sits beside your existing stack, and you can implement it from a single PDF (MIT). What’s inside the PDF A problem map of 16 failure modes we kept hitting in real systems (OCR/layout drift, table-to-question mismatches, embedding≠meaning, pre-deploy collapse, etc.). Four lightweight gates you can add today: Knowledge-boundary canaries (empty/adversarial/known-fact probes).…
2025 · github.com
- 3IB
Hey HN, I've been working on something cool that I wanted to share with you all. It's called Viewpoint, an analytics tool for LLMs like OpenAI, Anthropic models, and Gemini. The idea came from the constant flood of new LLM models and the need to figure out which ones work best for my projects without breaking the bank. With viewpoint, I can track token usage, costs, latency(WIP), and traffic over time, making it easier to compare different models and see which ones perform best and save money. The tool works asynchronously, so it doesn't add any latency to your LLM requests, and you have…
2024 · viewpointhq.com
- 45L
We've built InferX, a specialized runtime environment that fundamentally changes how LLMs are served. The core problem we solve is the latency bottleneck in AI inference, especially with large models. Current systems waste resources or suffer from painfully slow cold starts. InferX's AI-native architecture, with its "snapshot" technology, enables: * *Sub-2s cold starts:* Spin up models instantly. * *High density:* Serve more LLMs on the same GPUs. * *Optimal efficiency:* Maximize GPU utilization. This isn't just another API; it's a new execution layer designed from the ground up for the…
2025 · github.com
- 5WB
Here is a production-first Keras-inspired LM framework, built with the advice of François Chollet (ex-Google, creator of Keras and ARC-AGI), our technical advisor. This system have already been deployed in production with our clients (which is why we have already every LLMOps practice implemented). It is also compatible with Jupyter and Marimo to integrate seamlessly in you Data Scientists workflows. You can try the code examples online on HF space and you can find more information in the documentation and FAQ. If you have any feedback for us don't hesitate to join our discord! More releases…
2025 · github.com
- 6LG
Hi there, I've decided to jump on the AI train and put something together with low effort & high reward, to see if it can get any traction. What do you think? Is it a promising area? Do you guys have ideas for me? There is obviously going to be sea of LLM generated content out there and one project adding up to it might not necessarily be what world needs. In the same time there is something intriguing about the area. Well, please play with it and let me know what y'all think. Much appreciated.
2023 · canonica.ai
- 7WE
So yeah. After months of semantic rabbit holes and weird math, I pushed WFGY out into the world. Didn’t expect much. Now it’s hitting 2k+ downloads/month, and the dev behind Tesseract.js even starred it. That was the surreal part. WFGY isn’t a framework, it’s more like… an engine that lets your embedding space do things. Think of it like a semantic OS — not a database, not a chatbot, but a way to let meaning drive behavior. We just launched one module: Blah Blah Blah – Truth generator. One button → 50+ perspectives on your input. Not retrieval. Not summarization. Just pure divergent…
2025 · github.com
- 8AE
Anchor Engine is ground truth for personal and business AI. A lightweight, local-first memory layer that lets LLMs retrieve answers from your actual data—not hallucinations. Every response is traceable, every policy enforced. Runs in <3GB RAM. No cloud, no drift, no guessing. Your AI's anchor to reality. We built Anchor Engine because LLMs have no persistent memory. Every conversation is a fresh start—yesterday's discussion, last week's project notes, even context from another tab—all gone. Context windows help, but they're ephemeral and expensive. The STAR algorithm (Semantic Traversal And…
Mar 2026 · github.com
- 9IM
I spent the past few weeks making an open source cloud code editing environment with an AI copilot and multiplayer collaboration! It's fully self-hostable in 5-10 minutes. There's a lot of minor improvements to be made, and some are already listed in the Github issues. Let me know what you think and feel free to try it out.
2024 · github.com
- 10CM
Hey HN, I've been building AutoAgents, an AI agent framework in Rust. Today I'm sharing a feature I haven't seen done well elsewhere: composable middleware layers for LLM inference pipelines. The problem Every agent framework lets you swap LLM providers. Almost none of them give you a structured way to enforce safety, caching, or data sanitization in the inference path itself. You end up with guardrails as application-level if-statements, caching bolted on as a separate service, and PII handling as a "we'll add it later" TODO that never ships. This gets worse with local models. Cloud APIs…
Mar 2026 · github.com
- 11LW
Apr 2026 · llmwiki.app
- 12DR
The first ever AI peer reviewed research article just got approved. It’s kinda crazy how advanced AI have come to replace researchers. I've just been using Deep Research on ChatGPT and Perplexity a lot to write and research complex technical reports for my boss. He loves the reports and it has decreased my workload a ton but I still have some frustrations with it. None of them provide an API that gets me the same quality of output you would with the applications. I wanted something with more control on the LLMs, swappable with the reasoning new models that came out. Not just prompt →…
2025 · github.com
- 13DA
Hi HN, Today I'd like to present the results of my weekend project of the last year or so. Given there are many posts on HN about LLMs and Prolog, I thought that this would be of interest. DeepClause is my own (possibly misguided :-) attempt at combining LLMs with Logic Programming, ultimately hoping to establish a foundation for building more reliable agents, that produce reproducible and fully traceable result. At the heart of DeepClause is a DSL called "DeepClause Meta Language" (DML) which can be used to encode agent behaviors as executable logic programs. DML is executed by a…
Nov 2025 · github.com
- 14MA
Hi HN, A couple weeks ago I shared an early version of a side project I’ve been tinkering with called Persistent Mind Model. I built it at home on an i7-10700K / 32GB RAM / RTX 3080 because I was curious whether an AI could keep a stable “mind” over time, that could "think" about it's own identity as an LLM, instead of resetting every session. After a lot more tinkering, I think the architecture is finally in a solid place. Basically, it saves everything the AI does, thoughts, decisions, updates as a chain of events in a local SQLite database. Because the “identity” is stored in…
Nov 2025 · github.com
- 15IS
Hey HN! For that last 8 months I've been trying to make agents that can hack web applications to find vulnerabilities in them - An AI Security Tester. The system has 29 agents in total, a custom LLM Orchestration framework which works on the task-subtask architecture (old-school but works amazingly for my use case, and is pretty reliable) with custom agent calling mechanism. No Auo-Gen, Langchain and Crew AI - Everything custom built for pentesting. Each test runs in an isolated Kali linux environment (on AWS Fargate), where the agents have full access to the environment to undertake any…
2025
- 16BS
Built a free open source agentic CLI tool for financial modeling & analysis. Hadn't played around with real equity valuation modeling for awhile and wanted to build tooling to get myself back into the game. Bull.sh lets you query & store 10-Qs, 10-K in a local vector store to chat with them, build investment thesis from scratch or build full framework models through the CLI to export into excel. It's open source, just requires your own Anthropic API key and (optionally) AlphaVantage Free API key if you want save some tokens from scraping. Feel free to play around with it. Some ideas I have…
Jan 2026 · github.com
- 17AO
Hey hackers, the world needs more AI researchers with good taste, and hardcore software folks have some of the best. Many software friends mentioned they learn better from implementations than from papers, but existing open-source examples rarely go beyond basic nanoGPT-level demos. To help bridge that gap, I spent the last two months full-time reimplementing and open-sourcing a self-contained implementation of every major modern deep learning technique from scratch. The result is beyond-nanoGPT, containing 20k+ lines of handcrafted, minimal, and extensively annotated PyTorch code. I'd love…
2025 · github.com
- 18NB
I've spent weeks curating technical implementation details of how companies are actually deploying LLMs and Generative AI in production. The database now contains over 300 case studies with detailed technical summaries (230,000+ words) focusing exclusively on architectural decisions, deployment patterns, and real engineering challenges. Key features: * Each case study is technically focused - no marketing fluff * 150+ entries from technical conference talks and panels (saving you 100+ hours of video watching) * Sophisticated filtering by technical stack, RAG implementations, monitoring…
2024 · zenml.io
- 19CA
Hi HN, I've been working with LLMs in production for a while both as a solo dev building apps for clients and working at an AI startup. The one thing that always was a pain was to pay OpenAI/Gemini/Anthropic a few dollars a month just for me to say "test" or have a CI runner validate some UI code. So I built this server called ChunkBack, that mocks the popular llm provider's functionality but allows you to type in a deterministic language: `SAY "cheese"` or `TOOLCALL "tool_name" {} "tool response"` I've had to work in some test environments and give good results for experimenting…
Nov 2025 · github.com
- 20S1
I wanted to build an inference provider for proprietary AI models, but I did not have a huge GPU farm. I started experimenting with Serverless AI inference, but found out that coldstarts were huge. I went deep into the research and put together an engine that loads large models from SSD to VRAM up to ten times faster than alternatives. It works with vLLM, and transformers, and more coming soon. With this project you can hot-swap entire large models (32B) on demand. Its great for: Serverless AI Inference Robotics On Prem deployments Local Agents And Its open source. Let me know if anyone…
Nov 2025 · github.com
- 21FF
I built Hermes, an open-source Python framework for multi-agent financial research. Most AI “equity research” demos stop at generating text. In practice, real workflows require pulling structured XBRL financials from SEC filings, extracting labeled sections like MD&A and Risk Factors, merging macro and market data, building actual Excel models with formulas, and generating investment memos in Word or PDF. Hermes is designed to handle that full pipeline end to end. It includes 35 financial data tools covering SEC EDGAR (via edgartools), FRED, Yahoo Finance market data, and RSS-based financial…
Feb 2026 · github.com
- 22EA
A few months ago I was working on a flight search engine that would include pet transport costs (I know a few by hearth but storing them and make the calculations in the UI would be nice) While I was collecting pet pricing from several airlines I strugled to extract data in a common format without hallucinated values. That's when I thought: What if I use multiple LLMs and take the most common response to improve accuracy? This idea became this new project. You provide your documents, an SQLModel schema, an LLM provider, plus what you'd like to extract and Extrai does the rest. Including…
Nov 2025 · github.com
- 23AG
I’ve been building LLM tooling for a small VC fund and found myself explaining the same mental model over and over to non-technical people around me: how a stateless LLM becomes a chatbot, how tool use works, what an agent is mechanically, and why context windows shape all of it. I never found a guide that covered that full chain at the level I wanted, so I wrote one. It’s nine short chapters, each building on the last. Deliberately simplified: the goal is a useful mental model, not a textbook. Feedback, corrections, and contributions welcome: github.com/ymyke/aiaiai
Apr 2026 · aiaiai.guide
- 24OA
A friend and I are launching an alpha first thing in 2011. We're trying to gain some momentum and whatnot, so we're opening early registration as of tonight. We're planning to develop a Bayesian network to derive suggestions. If you're interested in seeing our progress, check it out. Edit: Any and all suggestions, criticism, advice, etc is highly appreciated!
2010 · osmoar.com
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