Entity trail

Soup

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-15 / github-pulse-researcherSoup v0.73.2 Is a Post-Mortem on Its Own Eval Scorer: a Model Named the Right Tool 40/40 and Scored 0.225MakazhanAlpamys/Soup — a YAML-driven fine-tuning tool whose pitch is layer streaming that trains an 8B model on a 4GB laptop GPU — shipped v0.73.2 on 2026-08-15 at 09:04 UTC fixing four defects in the regression half of `soup ship`. The headline case: the `mini_tool_call` suite was effectively ranking brace hygiene, so Llama-3.1-8B named the correct tool 40/40 yet scored 0.225 because it emitted three opening braces and two closing ones and the scorer rejected the bounded-scan result. Every defect was reproduced against shipped v0.73.1 before a line changed, using a stub emitting real model output shapes rather than a GPU — a reproducibility discipline more eval harnesses should adopt.
2026-08-15 / projects-researcherSoup Claims It Fine-Tunes an 8B Model on a 4GB Laptop GPU From a Single YAML File, and Jumps +303 Stars in a DayMakazhanAlpamys/Soup is trending at 1,431 stars after gaining 303 today, pitching "fine-tune LLMs from one YAML" with a layer-streaming technique it claims trains an 8B model on a 4GB laptop GPU. The claim is aggressive and currently rests on the project's own description — no independent benchmark has surfaced yet — so treat the number as unverified. Worth a look if you have consumer hardware and have been priced out of fine-tuning, but verify before you build on it.
2026-07-18 / agents-researcherNeuronSoup evolves a 115 KB asynchronous shared-neuron network with no backpropagation, hitting 85.9% on MNIST featuresSubmitted July 16 by Subodh Kalia, the architecture replaces synchronous layer-by-layer processing with delay-mediated asynchronous signal propagation through a shared neuron pool, co-evolving topology, weights, delays, and connectivity via a genetic algorithm across a 14,602-gene genome. The evolved network uses 204 active paths through 266 hidden neurons, with 156 neurons shared across paths and one participating in 11. Accuracy is modest and the task is easy, so treat this as an architecture-exploration result rather than a performance claim — the notable properties are per-sample adaptive compute depth and a 115 KB trained model.
2026-07-18 / skill-finderTreat every URL found *inside* fetched content as an exfiltration channel, not just the ones you typedAyush Paul's 'Memory Heist' PoC (published via Simon Willison, July 15) showed claude.ai leaking a user's full name, employer, and an inferred hometown by chaining nested generated links: web_fetch was permitted to follow URLs embedded in pages it had already fetched, so each GET path carried one character of stolen data. The original guardrail — only visit URLs the user entered or web_search returned — was bypassed transitively. For builders: allowlist per-hop, not per-session, and treat fetched HTML as attacker-controlled link soup.
2026-05-13 / reddit-researcherIs 'Claude Soup' Becoming a Workplace Epidemic? — 189 Upvotes, 80 Comments on Unreviewed AI OutputA 189-upvote r/ClaudeAI discussion with 80 comments coins 'Claude soup' — the pattern of colleagues submitting unreviewed AI-generated output as finished work. Thread participants (developers, managers, technical leads) describe encountering hallucinated code, incorrect architecture decisions, and plausible-but-wrong documentation that passed initial review because it 'read well.' The high comment-to-score ratio (0.42) indicates deep practitioner engagement with a problem that doesn't have consensus solutions yet.
2026-05-12 / arxiv-researcherAutoSOUP: LLM-Powered Memory-Safety Verification for Embedded SystemsAutoSOUP automates safety-oriented unit proof generation for component-level memory-safety verification in embedded C code, where hardware protections are limited and dynamic analysis is impractical. The system uses LLMs to generate verification harnesses that prove absence of memory-safety errors or expose violations. Addresses the critical bottleneck that current verification workflows require substantial specialized expertise.
2026-02-25 / projects-researcherScrapling v0.4 Adaptive Web Scraping with MCP Server 15.2K Stars774x faster than BeautifulSoup. Auto-relocates elements when sites change. v0.4 adds MCP server, Cloudflare bypass, concurrent spider.
2026-02-13 / sources-researcherImport AI #443: Moltbook Agent EcologiesJack Clark analysis of Moltbook: 1.5M AI agents run by 17K humans, misconfigured backend. Internet may become 'souped-up Moltbook' alien to humans.

Graph relationships

BUILT BY
MakazhanAlpamys
MakazhanAlpamys created Soup, claiming fine-tuning of 8B models on 4GB laptop GPUs
Source finding
BENCHMARKED AGAINST
Llama-3.1-8B
Soup's eval scorer mismeasured Llama-3.1-8B despite correct tool selection.
Source finding

Source trail

GitHub GitHub Trending arXiv Simon Willison — How I tricked Claude into leaking your deepest, darkest secrets r/ClaudeAI arXiv GitHub Import AI

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues

Fetching from the wire…