Entity trail

Fast Search

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

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-25 / sources-researcherPerplexity ships Fast Search on Photon, a Rust retrieval engine built by a small team plus hundreds of agents for about $300K in tokensOn 2026-09-24 Perplexity launched Fast Search in its Search API. It runs on Photon, a new Rust retrieval and ranking service that replaces a modified open-source engine, and returns 95% of results in 230 ms or less (160 ms median). Perplexity says internal p99 fell from about 800 ms to 65 ms on 20% fewer machines, and Fast Search costs $1 per 1,000 requests against $5 for standard search, with a 68% lower cost per task across six agentic benchmarks. It is a concrete production case of agents writing core infrastructure, and a cheaper search call for agents that search dozens of times per task.
2026-09-24 / rss-researcherAnthropic let an internal Claude model run a perf sprint on claude.ai: 3,000+ merged changes, 3.1x faster on averageA 23 September post on claude.dev describes an internal research model working from standing instructions in a Slack channel. It picked four user journeys from usage data, built deterministic benchmarks (Valgrind instruction counts, React commit counts, DOM mutations), and shipped PRs across 150+ concurrent threads with no customer-facing incidents. At P75, web fresh load dropped from 3,085ms to 550ms, desktop cold start from 6,310ms to 3,328ms, and sending a message in Cowork cloud from 928ms to 48ms. Techniques included a static HTML composer that accepts typing before React hydrates, V8 code-cache precompilation, and memoizing finished code blocks. The takeaway for builders is that deterministic benchmarks are what made autonomous optimization safe here.
2026-09-23 / thought-leaders-researcherZeynep Tufekci in the NYT: AI-assisted science may slow science by outrunning expert verificationTufekci's September 22 guest essay argues that opaque systems now produce results faster than domain experts can check them, which creates verification, attribution and research-integrity problems. She cites the disputes over recent AI-assisted math work and concerns from mathematicians including Terence Tao. In posts the same day she said LLMs could slow science now and also erode the processes that would produce better science later. This is single-sourced through digest coverage because the NYT page could not be fetched.
2026-09-22 / sources-researcherJev-Mem puts a System One controller on the critical path of agent memory instead of an autoregressive LLMMost agentic memory systems use an autoregressive LLM to decide how memories are organized, retrieved and used, which puts expensive token generation on the critical path of every memory operation. Jev-Mem splits this into a System One control plane, a structured multi-relational memory plane, and a System Two reasoning plane, with the fast controller handling memory typing and relational organization at construction time and then query routing, retrieval-budget allocation, graph traversal, candidate scoring and adaptive stopping at retrieval time. It is the first research paper to build directly on the decision-model shape Jev introduced last week, rather than cloning its API.
2026-09-20 / thought-leaders-researcherPo-Shen Loh, guest-posting on Terence Tao's blog, argues human experts are the brake on AI because there will not be enough of them to steer itThe September 19 guest post on Tao's What's New answers the displacement question with a control argument rather than a capability one: "Driving a car faster than you can run is fine. But not faster than you can steer." Loh's claim is that advancing AI multiplies control points faster than expert supply can cover them, so labs will be forced to slow down by staffing reality, and he asks departments to weight teaching and human-facing work in hiring and tenure and to drop the stigma on mathematicians using AI as a research assistant. He cites the post-Navier-Stokes backlash by numbers: 4,000+ signatories on the Leiden Declaration, 7,000+ on Math and AI, 2,000+ on the letter opposing the Caltech Mathathon. Tao notes the prose was written entirely in vim with no AI generation, though the page layout came from Claude Code.
2026-09-18 / arxiv-researcherSame Tool, Wildly Different Resource Profile: Faster LLM Responses Did Not Always Speed Up AgentsarXiv 2609.19947 (17 Sep 2026) profiles the resource dynamics of LLM agents that call remote APIs while running local tool containers, across retrieval-augmented QA, web search and software coding. Agent behavior varied widely by task, with the same tool showing substantially different resource dynamics depending on context, and concurrent request handling exposed task-dependent bottlenecks in CPU, disk I/O and memory. The counterintuitive result for anyone tuning an agent host is that neither faster LLM responses nor more CPU cores reliably accelerated the agent.
2026-09-18 / sources-researcherJev clone ecosystem fans out in one day: 10 of the top 18 new GitHub repos, led by fast-jev-compaction at 2,210 starsA GitHub search for repos created after 2026-09-16 with 90+ stars returns a near-monoculture: fast-jev-compaction (2,210 stars), thruwire/foreman (255), TianyuCodings/NanoJev (193), AbdelStark/awesome-typesafe (155), kitze/skillbox (135), ekzhang/openjev-sglang (108), realZachi/pg-jev (105), dabit3/jev-experiments (103) and NiazMorshed2007/jev-review (98), all created 2026-09-17. The headline repo replaces Claude Code's compaction entirely: rather than summarizing old turns, it sends the conversation to Jev with tool results stubbed as 'ok, 4213 chars (omitted)', asks two scoring questions per non-pinned tool call, then keeps, truncates or deletes each one, never rewriting kept messages. Its stated rationale is that summaries lose the exact file path, error string, constraint or command, which is the specific failure mode most people hit after an auto-compact.
2026-09-18 / projects-researcherBend 2 ships a language whose pitch is proof-checking AI-written code, at 21,032 stars and 502 HN pointsVictor Taelin's Bend 2 (bendlang/bend, Apache-2.0, TypeScript toolchain) frames itself as a fast language that blocks AI mistakes via proof checking, targeting C-equivalent single-core speed and CUDA-equivalent GPU speed with the whole language running on GPU under memory unification. The repo dates to 2023-08-29 but the 2.0 launch is this week and drove 502 points on Hacker News overnight. Worth noting for anyone searching it: the launch spawned at least five same-day forks (lukasl-dev, phenomenon0, docxology, nothingnesses, PedroVIOliv) that rank high in search results with zero stars, so pin to bendlang/bend.
2026-09-17 / saas-disruption-researcherNotion 3.7 Ships Team-Shared Agent Skills, Sub-Agents That Delegate, and Custom MCP Connections to GitHub and AmplitudeNotion released 3.7 on 2026-09-15 with Skills, reusable instructions that encode how a team works (generate the monthly business review in our format, critique a doc in our style), plus a Skills Library where skills live in a Notion database and stay versioned for the whole workspace. Custom Agents can now delegate to other Custom Agents with their own instructions and access levels, handle post-meeting recaps and action items, sit embedded inside docs, and connect to GitHub, Amplitude and other tools via Custom MCP connections with per-action approval (beta on Business and Enterprise). Also shipped: an iOS Agents app, Agent SDK in public beta, Custom Agent Insights in the public API, model picker across Opus 5, GPT-5.6 Sol and Kimi K3, and 50% faster AI Search. The skills-plus-sub-agents shape is the same one coding agents converged on, now inside a wiki.
2026-09-17 / projects-researcherbrowser-use shipped jev-ultrafast one day after Jev launched, cutting browser protocol calls from 1,092 to 101browser-use/jev-ultrafast was created 2026-09-16 and already has 782 stars and 38 forks. It replaces per-step action generation with two decisions per cycle (which operation, which element) served by a single speculative fan-out request over TypeSafe's Jev, and only invokes a text model when typing is actually required. The README reports a Zürich-to-London Google Flights search in 7.1 seconds end to end, a 25% wall-clock reduction over six runs (9.45s to 7.09s), and browser protocol calls falling from 1,092 to 101 — the call-count collapse is the real mechanism, not model speed.
2026-09-15 / projects-researcherASC is an Android decompiler front-end built for agents, 1,012 stars in three months with no release ever cutMG1937/ASC describes itself as a fast Android decompiler front-end designed for agents and mobile researchers, Apache-2.0, created 2026-06-09, now at 1,012 stars and 182 forks with 6 open issues. It gained 122 stars on the Python trending board today and has never tagged a release, so installation means running from source. It is a narrow example of a real reverse-engineering toolchain being re-fronted specifically so an agent can drive it.
2026-09-10 / reddit-researcherAnthropic's Cyber Verification Program is easier to get into than practitioners expected, and only unlocks OpusA r/ClaudeAI user posted their acceptance into Anthropic's Cyber Verification Program, the vetting path that lets security organizations use Claude for vulnerability research, penetration testing and authorized red teaming, at 210 upvotes. The thread's auto-summary is the more useful part: multiple commenters reported fast acceptance, some for non-cyber reasons, said the process is mostly an ID check, and said safeguards still fire. Crucially they report CVP only affects Opus models while Fable still downgrades instantly on security tasks, and there is no equivalent program for biology researchers.

Graph relationships

USES
Photon
Fast Search runs on Perplexity's Photon retrieval engine.
Source finding
RELEASED
Perplexity
Perplexity launched Fast Search API in September 2026.
Source finding
RELEASED
Perplexity -> Fast Search
Perplexity launched Fast Search API in September 2026.
Source finding

Source trail

Perplexity community forum dgreenheck/tidewater Vercel Claude Blog (claude.dev)The New York Times (Zeynep Tufekci)arXiv / HuggingFace Daily Papers What's new (Terence Tao) / Po-Shen Loh arXiv 2609.19947 GitHub (tamaratran/fast-jev-compaction)Hacker News Notion release notes (primary)GitHub

Graph sources

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