Alternatives
Products that do what CTHmodules does
data, time, analytics, science, psychohistory, Asimov, tech
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- 3AF
2016 · github.com
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Clearer decisions in life and work using ancient wisdom
Jul 2026 · themodernshrine.com
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- 6PS
Hi HN, I turned the freshly published paper “The Constructor Theory of Time” by David Deutsch and Chiara Marletto (arXiv, 13 May 2025) into an executable Python library. What you’ll find • One-to-one translation of the paper’s formalism: Substrates, Attributes, Tasks, Constructors, and task-algebra operators • Possibility / impossibility predicates and counterfactuals encoded exactly as defined • Test suite that mirrors every lemma and example (>95 % coverage, mypy-typed) • Reproductions of key results: time-keeping substrates, irreversibility proofs, quantum branching tasks, and a…
2025 · github.com
- 7OS
Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…
2023 · vectara.com
- 8MA
`mandala` is a framework I wrote to automate tracking ML experiments for my research. It differs from other experiment tracking tools by making persistence, query and versioning logic a generic part of the programming language itself, as opposed to an external logging tool you must learn and adapt to. The goal is to be able to write expressive computational code without thinking about persistence (like in an interactive session), and still have the full benefits of a versioned, queriable storage afterwards. Surprisingly, it turns out that this vision can pretty much be achieved with two…
2024 · github.com
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I built Living Memory Dynamics (LMD), a Python framework for simulating biologically-inspired "living" episodic memory directly in embedding space—no external LLM required for the core dynamics. Memories evolve over time like living entities: they have metabolic energy states (vivid → active → dormant → fading → ghost), emotional trajectories, and resonance fields that let them influence each other. The central piece is a new differential equation I derived (the Joshua R. Thomas Memory Equation) that drives continuous-time evolution: dM/dt = ∇φ(N) + Σⱼ Γᵢⱼ R(vᵢ, vⱼ) + A(M, ξ) + κη(t)…
Jan 2026 · github.com
- 10TT
Simulate anything on a map from a text prompt -- and conduct risk analysis against LiveUA map's global realtime data points from social media and news sources. I trained a GPT-2-size model on historical incident data used to predict things that will go wrong. As historian Benjamin Breen mentions, the leading language models are good historians, so the application will simulate historical events pretty well also. I include a Multi-Agent RL Urban Mobility model in progress displayed on the map as small white cubes representing traffic and pedestrians. Around SF, it uses real census data and…
2025 · mused.com
- 11HA
2021 · github.com
- 12AW
I've been presenting at local meetups about Context Engineering, RAG, Skills, etc.. I even have a vbrownbag coming up on LinkedIn about this topic so I figured I would make a basic example that uses bedrock so I can use it in my talks or vbrownbags. Hopefully it's useful.
Apr 2026 · github.com
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2016 · the-codex.net
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Local, gradient-free neuro-symbolic memory engine combining Hyperdimensional Computing (HDC/VSA), Hebbian plasticity, and graph triples for offline AI. - roandejager/Hillock
7d ago · github.com
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I have a proposal that addresses long-term memory problems for LLMs when new data arrives continuously (cheaply!). The program involves no code, but two Markdown files. For retrieval, there is a semantic filesystem that makes it easy for LLMs to search using shell commands. It is currently a scrappy v1, but it works better than anything I have tried. Curious for any feedback!
Apr 2026 · github.com
- 18SA
Hey HN, I’m a physicist turned quant. Some friends and I 'built' SymDerive because we wanted a symbolic math library that was "Agent-Native" by design, but still a practical tool for humans. It boils down to two main goals: 1. Agent Reliability: I’ve found that AI agents write much more reliable code when they stick to stateless, functional pipelines (Lisp-style). It keeps them from hallucinating state changes or getting lost in long procedural scripts. I wanted a library that enforces that "Input -> Transform -> Output" flow by default. 2. Easing the transition to Python: For many…
Feb 2026
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- 20IB
I built Chronoscope, a project to explore the world through time. I've been wanting to do this for a while, after being inspired by Ollie Bye's "History of the World" video several years ago. I'm not the first person to have done this - resources like OpenHistoricalMaps are amazing. But, I noticed there were a few disparate datasets / academic databases online, so I combined them together as best as I could (I've linked all sources in the app). To make it more interesting, I also included: - Notable events from the time period (geolocated where possible), sourced from wikidata - Ancient…
Mar 2026 · shiphappens.xyz
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Any initial thoughts? This framework is meant to be a tool for construction, so if you want to play around with it for creating potential specific implementations, you can drop the contents of the website, the GitHub README, and the entire overview.md into an AI chat, and that should be enough to use the framework, at least conceptually. Would y'all want me to pre-prime a chat in Google AI Studio with the full context of the plan and some basic direction for discourse? I can share a link to a ready-to-go environment. The core documentation should answer most mechanical questions. And if you…
Oct 2025 · worldamazing.org
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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
- 24CA
Synthetic data generation is an essential step in training and evaluating LLMs/Agents/RAG pipelines, but tooling around this is still lacking. We're introducing Curator, an open-source library designed to streamline the data curation process. While there are many libraries to prompt LLMs, the semantics of generating synthetic data is different from prompting. For example, we need to process a large number of prompts (sometimes in millions or more) while accepting some failures, utilize several stages of prompting, incorporate human feedback, and filter out bad data using verifiers…
2025 · github.com
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