Alternatives
Products that do what Risk-Cascade_Analyzer does
See the domino chain before the first tile falls
- 1TC
Hello, I wanted to share with you all a interactive map of the economics and physics constraints of the AI buildout. It has macro drivers, industrial chokepoints, and where that shows up in markets. I've added 393 nodes and 562 edges to capture other supply / physics constraints as well. There's no sign up, and no pay wall, it's all free. Please let me know what you think!
Jun 2026 · atomprophet.io
- 2IS
OP here: I created this visualization tool as the byproduct of a supply chain class I taught at Columbia. The pedagogical exercise grew into a full blown visualization and paper about global oil trade. The model: The mechanics are the same as the financial network Eisenberg-Noe: Instead of banks, every country consumes oil interconnected via bilateral trading. Shocks propagate throughout the network, depleting oil reserves when bottleneck nodes (such as the Strait of Hormuz) are blocked. Insights: The interesting part is the mechanics of how the crisis unfolds: for example, France receives 0…
Jul 2026 · globaloilnetwork.staffinganalytics.io
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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
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Hi Hacker News! I’m a Bayesian statistician that has been working on applying hierarchical mixture models (originally developed for genomics) to structure text data, and in the process, used these models to build (what started as a personal) tool for conducting literature reviews and deep research. My literature review process starts with a broad search to find a few key papers/groups, and from there expands along their citation networks. I needed to conduct a few rounds of literature reviews during the course of my research and decided to build a tool to facilitate this process. The…
Oct 2025 · sturdystatistics.com
- 8NP
Neural CAs model self-organizing pattern formation on grids. Now the grid is gone. Each cell is an agentic particle that can move freely in space and change its state. While each particle follows a simple shared rule, many together can grow complex morphologies or form intricate patterns. The resulting particle system as a whole can regenerate from damage and exhibits surprising emergent behavior. Try cutting the lizard and watch it heal itself!
Jun 2026 · selforg-npa.github.io
- 9SA
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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2019 · clearbrain.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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Hello HN! We're Sam and Kanyes. We're building an extension to help you remember what you read online. We're calling it Ferret [1]. When you open Ferret on an HTML page, it generates recall-based questions + answers to reinforce key concepts with NLP. Consider the following toy example where we open Ferret on an explanation of Bayesian statistics. [2] Q: What does the frequentist interpretation view probability as? A: the limit of the relative frequency of an event after many trials Q: What is often computed in Bayesian statistics using mathematical optimization methods? A:The maximum a…
2021
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Mar 2026 · github.com
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Most consensus libraries (Raft, Paxos) treat the state machine as a pure black box. This is fine until your state machine needs to actually do something, like charge a credit card, fire a webhook, or send an email. If a leader crashes after the side effect but before committing it, you get duplicates. This is my attempt at fixing this problem from first principles ish: build chr2 to make crash-safe side effects first-class citizens. mechanism: Replicated Outbox: Side effects are stored as "pending" in replicated state. Only the leader executes them under a fencing token. Durable Fencing: A…
Jan 2026 · github.com
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Hi HN, Over the past two years I’ve built and debugged a fair number of production pipelines—mainly retrieval‑augmented generation stacks, agent frameworks, and multi‑step reasoning services. A pattern emerged: most incidents weren’t outright crashes, but silent structural faults that slowly compromised relevance, accuracy, or stability. I began logging every recurring fault in a shared notebook. Colleagues started using the list for post‑mortems, so I turned it into a small public reference: 16 distinct failure modes (semantic drift after chunking, embedding/meaning mismatches,…
2025 · github.com
- 22BA
I’ve been experimenting with a graph-based approach to a classic trading problem: why most dip-buying strategies can’t tell the difference between a temporary overreaction and a genuine structural collapse. Most systems treat a −5% move the same regardless of context. My hypothesis was that where a company sits in the market’s structure matters more than the price move itself. The engineering idea I built a knowledge graph of the U.S. public markets with ~207k edges across ~21 relationship types, organized into four layers: Operational: supply-chain relationships (SUPPLIES_TO, PRODUCES)…
Dec 2025
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Members can now see how life decisions could reshape the probability of success of their retirement plans in real time.How Range rewrote its Monte Carlo simulation orchestration layer in Golang to cut simulation time ~39% and power a live, interactive retirement plan.
12d ago · range.com
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Hey HN, We explored how decision-making happens under severe information asymmetry and used career exploration as a test subject. In practice, people converge on a small set of highly legible default paths, with little visibility into credible alternatives. That’s why we’re building Fig, a tool for reasoning about career paths when the next step isn’t obvious, given the dependence on fleeting personal preferences. Most existing tools respond by collapsing uncertainty into a single recommendation or by modeling careers as linear trajectories. That works reasonably well at the recruiting…
Jan 2026 · figcareer.com
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