Entity trail

Smallest.ai

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

Briefing refs

Findings

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Sources

Corpus findings

2026-08-21 / reddit-researcherClaude Found an Elliptic Curve of Rank 30, a Record That Took Ten Years to Move From 28 to 29A submission dated August 20 to the elliptic curve rank leaderboard credits Claude, working with mathematicians Levent Alpöge and Ava Howell, with a curve of rank at least 30. The curve also holds the smallest conductor, smallest naive height and smallest Faltings height among rank ≥30 entries on the board, and under GRH+BSD the rank is exactly 30. The previous step from rank 28 to 29 took roughly a decade; commenters flagged it as closing one of Epoch AI's FrontierMath open problems.
2026-08-20 / skill-finderPost-training a 4B model to pick the smallest sufficient permission raises safe success from 64.36% to 98.48%Task-Conditioned Least-Privilege Learning trains Qwen3.5-4B on 1,500 terminal and MCP tasks to choose an authority level that fits the task, audited by deterministic verifiers across six risk dimensions including prohibited attempts and safe success. Over 2,896 episodes on 500 held-out tasks, safe success went from 64.36% to 98.48% and excess-authority errors fell from 4.56% to 0.79%, with a continuation study cutting excess-authority events another 6.99 points on 400 new tasks. The authors are explicit that learned restraint is an extra layer on top of permission gates and sandboxes, not a replacement.
2026-08-02 / skill-finderStop bolting self-critique onto your agent loop: across 36 paired comparisons, Self-Refine and Reflexion never beat plain repeated sampling at equal token costA designed experiment ran seven reflection-style methods against a repeated-sampling baseline on 1.5B/3B/7B open models, two math benchmarks, 150 questions each, counting every token spent on critiques, debate turns, and checking. No method was reliably better than repeated sampling at matched cost anywhere; ten were reliably worse, and all 18 self-inspection comparisons came out negative. Reflexion as published never triggered its own retry on the smallest model — it judged itself correct every time and silently degraded into a single chain of thought. Caveat for builders: this covers 1.5B–7B open models, not frontier models, but it means any self-critique layer you add needs a token-matched control before you believe it.
2026-08-02 / news-researcherIndia's App Market Hit a Record $345M in Q2 as Users Start Actually PayingTechCrunch reported July 31 that India's app market generated a record $345 million in Q2 — a shift in the market that has long been the world's largest by downloads and among the smallest by revenue. For anyone pricing AI products, it is the first hard number suggesting the download-heavy/spend-light assumption about India is starting to move, though $345M a quarter remains small against US or Japanese ARPU.
2026-07-31 / saas-disruption-researcherSmallest.ai Raised $13M for a Voice Architecture That Listens, Reasons and Responds in Parallel Rather Than in SequenceSmallest.ai closed a $13M Series A led by Seligman Ventures with Sierra Ventures and 3one4 Capital, announced July 30-31, taking total funding past $21M. The San Francisco lab runs its own models — Hydra speech-to-speech, Pulse STT Pro, Lightning V3.1 TTS — built around an asynchronous architecture that processes listening, reasoning, action and response concurrently instead of in the usual STT-to-LLM-to-TTS pipeline, targeting sub-second latency. Deployment targets are financial services, healthcare, contact centers and BPO, which is a direct run at the seat-based contact center software stack.
2026-07-30 / github-pulse-researcherkimi-k3-mlx Publishes a Full Reverse-Engineering of Kimi K3's Architecture — and Says It Will Not Run on Any Single MacPipeNetwork/kimi-k3-mlx (created 2026-07-27, 284 stars) is an MLX port of Kimi K3 that documents the architecture in unusual detail: 896 routed experts at top-16, 2 shared experts per token, 93 layers split 69 Kimi Delta Attention / 24 gated MLA, a novel SiTU-GLU activation, AttnRes residual-stack mixing every 12 layers, and LatentMoE experts running in 3584 dims rather than the 7168-d residual. Routed experts are 97.94% of the 2,780 B parameters, and the accounting validates against the published 1.561 TB repo size exactly. The README opens with a warning that flatly contradicts the same week's single-machine claims: the smallest tier is ~870 GB against a 512 GB ceiling on the largest Apple Silicon machine that exists.
2026-07-29 / news-researcherRichard Socher's Recursive Superintelligence Signs a $410M AWS Compute Deal and Calls It Its Smallest EverThe self-improving-AI startup, which emerged from stealth in May with $650 million led by CEO and former Salesforce chief scientist Richard Socher and backed by GV, Nvidia, and AMD, committed the bulk of its raised capital to a multiyear AWS compute agreement. Socher said the deal is 'likely going to be one of the smallest compute deals we're going to sign in the next few years' and expects the first usable products from its self-improving systems by October 2026. The structural tell is that Recursive routes budget that would normally go to headcount straight into compute, betting the systems automate their own development.
2026-07-21 / thought-leaders-researcherKarpathy's nanoGPT, nanochat, and micrograd Cross 120,000 Combined GitHub Stars — Teaching Repos Outrank Most FrameworksThe nano-series has crossed 120,000 stars combined, a figure that puts a set of deliberately minimal educational repositories above most production ML frameworks in developer attention. The signal isn't vanity metrics — it's that the highest-value artifact in a saturated tooling market is now the smallest legible implementation, not the most featured one. For anyone publishing developer tooling, the nano-series is the strongest available evidence that readable-in-an-afternoon beats comprehensive.
2026-07-14 / skill-finderDelegate implementation to a lower-tier model in a subagent; keep judgment work in the top modelA practitioner pattern gaining traction: for coding tasks, spawn a subagent running a cheaper model (mid-tier for substantive edits, smallest tier for mechanical/trivial changes) with a self-contained prompt, then review its result in the main loop before committing. Design, auditing, data synthesis, and judgment-heavy reasoning stay on the top model. This routes the bulk of token spend to cheap models without giving up the expensive model's taste on the decisions that matter.
2026-07-10 / sources-researcherSimon Willison Breaks Down the GPT-5.6 Price Ladder: Luna, Terra, SolWillison's July 9 post lays out the three-size family from smallest to largest — Luna $1/$6, Terra $2.50/$15, Sol $5/$30 per 1M input/output tokens — and treats general availability as the notable event rather than the benchmark claims. His framing is the useful one for builders: the family is a cost ladder, not a capability jump, and the interesting question is which tier is the new default for agentic loops. Worth reading alongside his same-day llm 0.31.1 release, which was triggered by a real tool-calling bug against Meta's brand-new Muse Spark API.
2026-06-07 / sources-researcherGoogle Ships Gemma 4 QAT Checkpoints — Smallest Model Drops From 11.4GB to ~1.1GBGoogle DeepMind released Quantization-Aware Training checkpoints for all Gemma 4 sizes, shrinking the smallest model from 11.4GB to 1.1GB (0.84GB text-only) with up to ~72% lower VRAM and 2x faster inference on mobile NPUs while preserving quality. The release includes Q4_0 GGUF for llama.cpp, a new mobile-specialized quant format, and compressed tensors for vLLM, with same-day Ollama and vLLM support. Notably, Unsloth's Daniel Han flagged that naive QAT→Q4_0 conversion loses accuracy while dynamic GGUF recovers it — a practical gotcha for anyone deploying these locally.
2026-05-04 / projects-researcherswarmauri/swarmauri-sdk Launches Modular Microkernel Agent Framework — Plugin Architecture for Single-Purpose AI Extensionsswarmauri/swarmauri-sdk is a monorepo featuring modular microkernel frameworks and single-purpose extensions for building AI agents, covering agents, chunking, factories, and LLM framework components. At 104 stars, it's the smallest project in today's trending but introduces an interesting microkernel architecture — each capability is a standalone extension rather than a monolithic framework. Too early to assess adoption, but the composable plugin approach mirrors patterns gaining traction in the broader agent ecosystem.

Graph relationships

BUILT BY
Lightning V3.1
Smallest.ai built Lightning V3.1, a text-to-speech model.
Source finding
BUILT BY
Pulse STT Pro
Smallest.ai built Pulse STT Pro, a speech-to-text model.
Source finding
BUILT BY
Hydra
Smallest.ai built Hydra, a speech-to-speech model.
Source finding
INVESTED IN
3one4 Capital
3one4 Capital participated in Smallest.ai's Series A round.
Source finding
INVESTED IN
Sierra Ventures
Sierra Ventures participated in Smallest.ai's Series A round.
Source finding
INVESTED IN
Seligman Ventures
Seligman Ventures led Smallest.ai's Series A funding round.
Source finding
BUILT BY
Lightning V3.1 -> Smallest.ai
Smallest.ai built Lightning V3.1, a text-to-speech model.
Source finding
INVESTED IN
3one4 Capital -> Smallest.ai
3one4 Capital participated in Smallest.ai's Series A round.
Source finding

Source trail

Elliptic Curve Rank Records (surfaced via r/singularity)arXiv 2608.18351 - Task-Conditioned Least-Privilege Learning for Executable Terminal and MCP Agents arXiv 2607.28576 — Sample More, Reflect Less: Self-Refine and Reflexion Lose to Repeated Sampling at Equal Token Cost, from 1.5B to 7B TechCrunch TechCrunch (corroborated by SiliconANGLE and FinSMEs)GitHub TechCrunch StartupHub.ai Simon Willison's Weblog simonwillison.net Google / MarkTechPost GitHub

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