Entity trail

GPT-2 124M

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

Briefing refs

Findings

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Sources

Corpus findings

2026-08-23 / reddit-researcherAn English-to-Claudish Translator Is a Demo of Compiling Natural Language Specs Into 22MB Neural Programs That Run on CPUResearcher Yuntian Deng built a bidirectional translator between plain English and the over-engineered register people now call 'Claudish', and it hit 2,105 upvotes on r/ClaudeAI. The joke is the wrapper; the technique underneath is ProgramAsWeights, which finetunes a spec into a standalone neural program. The MIT-licensed Python SDK ships two compilers: Qwen3 0.6B producing roughly 22 MB programs at 0.05-0.5s per call, and GPT-2 124M producing roughly 5 MB programs at 0.03-0.3s per call, both running locally with no API key after setup.
2026-08-23 / hn-researcherPrime Intellect Ran 153 Autonomous Research Runs Across 18 Frontier Models and Closed 82% of the nanoGPT Human Record GapPrime Intellect published the NanoGPT Speedrun Frontier, which it calls the largest open experiment on how frontier models do AI research: 153 autonomous runs across 18 frontier models, each sandboxed on 8xH200s for up to eight days with no internet access, iterating only on optimizer hyperparameters for a 124M GPT training recipe from a shared baseline. The best runs closed 82% of the gap to a record dozens of humans built over months; the leaderboard's top entry is Fable 5 at 2,726, listed as 81.7% closed in 8.7 days on 800M tokens. Code is on GitHub at PrimeIntellect-ai/experiments-autonomous-speedrunning.
2026-08-20 / reddit-researcherSomeone Pre-Trained a 1.02B Kimi-K3 Replica on 5 Billion Tokens for $250 and Beat GPT-2 124M on HellaSwagA builder rented GPUs on Modal and pre-trained a 1.02B-parameter model (145M active per token) on exactly 5,000,003,584 decontaminated tokens for $250, keeping K3's actual architecture including Kimi Delta Attention, Gated MLA, attention residuals, LatentMoE with the aux-loss-free balancer, and K3's unmodified 163,840-token tokenizer. It scores 33.4% on HellaSwag against GPT-2 124M's 28%, with no instruction tuning at all. The model is roughly one two-thousandth of K3 by size, and the full tutorial plus a companion guide to hosting the real 2.8T K3 on eight B300s is published on books.vizuara.ai.
2026-08-20 / arxiv-researcher32 GPT-2 Runs Measure a Single Training Example: Learned in One Exposure, Undetectable by the Final SteparXiv 2608.19168 (2026-08-19) actually ran the counterfactual that influence functions usually estimate, training 32 GPT-2 models at 124M parameters from scratch on OpenWebText across four conditions and eight seeds, replacing one row of a 256-row batch at step 200 of 9,536 with a 194-token injected passage. Fifty steps later the injected arm predicts the passage better by 0.039 and 0.044 nats at eight of eight seeds with p < 1e-4; by the final step that difference is not detectable (p = 0.25 and 0.71) against minimum detectable effects of 0.025 and 0.079 nats. Weight displacement reaches 44.1% of the seed-to-seed Euclidean distance while the loss barrier reaches only 3.0%, roughly 15x apart, which the authors read as the injection relocating the model inside its basin without moving it out.

Graph relationships

USES
ProgramAsWeights
ProgramAsWeights compiler uses GPT-2 124M as an alternative backend.
Source finding
USES
ProgramAsWeights -> GPT-2 124M
ProgramAsWeights compiler uses GPT-2 124M as an alternative backend.
Source finding

Source trail

r/ClaudeAI (2,105 upvotes, 81 comments); github.com/programasweights/programasweights-python and Yuntian Deng on X Prime Intellect / Hacker News r/LocalLLaMA / books.vizuara.ai arXiv 2608.19168

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