Alternatives
Products that do what GitHub does
Deterministic valuation model for token ecosystems
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We've open-sourced Klarity - a tool for analyzing uncertainty and decision-making in LLM token generation. It provides structured insights into how models choose tokens and where they show uncertainty. What Klarity does: - Real-time analysis of model uncertainty during generation - Dual analysis combining log probabilities and semantic understanding - Structured JSON output with actionable insights - Fully self-hostable with customizable analysis models The tool works by analyzing each step of text generation and returns a structured JSON: - uncertainty_points: array of {step, entropy,…
2025 · github.com
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Hi HN! We’re been working hard on this low-code tool for rapid prompt discovery, robustness testing and LLM evaluation. We’ve just released documentation to help new users learn how to use it and what it can already do. Let us know what you think! :)
2023 · chainforge.ai
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Apr 2026 · github.com
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2014 · balances.io
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2020 · 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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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…
2025 · github.com
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2018 · cryptoproof.org
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I’ve been working on a solution to the "Physical Oracle Problem" (trustless trading of physical goods) and just released the full Alpha implementation. The Core Thesis: Existing decentralized marketplaces rely on reputation, which inevitably centralizes. Dealta replaces reputation with a Nash Equilibrium-based mechanism. We use staked, pseudo-randomly selected "Brokers" to physically verify goods. The protocol ensures that honesty is the dominant strategy for all actors via strict payoff matrices. It intended use is preferably trading of mid to high value goods. Nobody expecting a computer,…
Jan 2026 · github.com
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Hi HN, I'm Paul from Tensordyne. We build AI inference systems and chips on logarithmic math. We've put together an interactive Token Economics Calculator to help make apples-to-apples comparisons of inference hardware across vendors: We're interested in how closely it lines up with the community's view of the market. Why we built this Investors and customers kept asking how our system compares to others (NVIDIA and a growing list of startups). Plenty of publicly available data exists, but it's scattered and inconsistent. News articles, provider sites, Artificial Analysis, MLCommons, and now…
Nov 2025 · tensordyne.ai
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Abstract. Token-funded bounties to incentivize competition-style participation in blockchain development projects would result in wider and better allocation of investment, and allow investors to diversify exposure across a field of potentially successful projects. Overall this would increase the rate of progress within development of the blockchain space. We propose a centralised platform for the administration of token-funded bounty driven development projects whereby experts allocate pools of invested capital according to the technical merits of development projects, and investors are…
2017
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LLMs are better at being the "mouth" than the "brain" and I can prove it mathematically. I built a deterministic graph engine that offloads reasoning from the LLM. It reduces token usage by 89% and makes a tiny 0.8B model trace enterprise execution paths flawlessly. Here is the white paper and the reproducible benchmark.
Mar 2026 · github.com
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I formalized the Single Source of Truth (SSOT) principle in Lean 4 (~2.1k LOC, zero sorry) and proved two core results: Structural SSOT is achievable only when a language provides definition-time hooks and runtime introspection. Macros/codegen (before definition) and reflection (after definition) are insufficient. These requirements are derived, not chosen: because structural facts are fixed at definition, derivation must occur at definition time and be introspectable to verify DOF = 1. Would appreciate review, critique, or independent checking of the Lean scripts.
Jan 2026 · zenodo.org
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I'm Arthur, and I wanted to share an MVP for Marqt.org, which lets you crowd-source the truth. John Stuart Mill said that "Truth emerges from the clash of ideas." In that spirit, Marqt brings two adversarial sides together to quantify truth and showcase the best arguments for each side. It is inspired by markets, where buyers and sellers discover a product's true price and update it dynamically. The ultimate aim is to build an open-source semantic knowledge base that represents the collective wisdom of humanity in real-time. If we can do this, I believe it can solve the problem of…
2023 · marqt.org
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2018 · github.com
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