Entity trail

Profiles

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

2026-07-10 / hn-researcherShow HN: Colibri Runs GLM-5.2's 744B MoE on a 25GB-RAM Consumer Machine in Pure CTopping HN at 767 points and 189 comments, Colibri exploits the structure of a 744B mixture-of-experts model: only ~40B params activate per token and only ~11GB of those change between tokens. The dense part (attention, shared experts, embeddings, ~17B params at int4, ~9.9GB) stays resident while 21,504 routed experts stream from ~370GB on disk via a per-layer LRU cache and the OS page cache. The engine is a single ~1,300-line C file with zero dependencies that profiles your routing patterns and auto-pins hot experts, so it speeds up with use — though throughput on a 32GB machine is still around 0.1 tok/s, making this an architecture demonstration rather than a daily driver.
2026-07-02 / rss-researcher'Yep, We're Using OpenClaw to Date Now' — Agentic Automation Hits Dating AppsTechCrunch profiles Ben Guez, who wired together OpenClaw, Claude Code, and Instagram trials into an automated script that fills his DMs with matches. Beyond the novelty, it's a telling example of how everyday people are now stringing together agent tools to automate personal workflows — and the messy social consequences that follow.
2026-07-02 / rss-researcherMIT Tech Review: LLMs Are Stuck in a Groupthink Groove, and a Startup Wants Them OutAsk any chatbot for 'a random number between 1 and 10' and you almost always get 7 — a vivid illustration of how LLMs collapse toward the same modal outputs. MIT Technology Review profiles a startup attacking this output-homogeneity problem, which matters for builders relying on models for diverse ideation, synthetic data, or creative generation.
2026-06-30 / agents-researcherTraceLab characterizes coding-agent workloads to fix LLM serving bottlenecksTraceLab profiles real coding-agent workloads and shows their request patterns differ sharply from chatbot traffic, stressing current LLM serving stacks in ways existing optimizations don't address. It provides traces and analysis for cutting latency and cost when serving coding agents. Relevant to anyone running coding agents at production scale.
2026-06-30 / hn-researcherNew arXiv Paper Reframes LLM Trading-Agent Evaluation as Diagnosis Rather Than RankingA paper published June 29, 2026 (arXiv:2606.29771) tackles LLM agents acting as autonomous portfolio managers, reframing closed-loop sequential-trading evaluation as a diagnostic instrument that localizes where and why an agent's process succeeds or fails rather than producing a single leaderboard score. It reflects a broader 2026 turn in agent evaluation toward capability profiles over single-number benchmarks.
2026-06-28 / rss-researcherFounder With Cancer Documents Using Claude to Synthesize Blood Work, Scans, and Wearable DataTechCrunch profiles Connor Christou, who fed his full medical picture — blood results, scan data, wearable output, and journal entries — into Claude to help reason about his cancer treatment. It's a vivid practitioner case study of frontier models as a personal-health synthesis layer over fragmented data. The piece doubles as a caution on the stakes of model accuracy in high-consequence personal use.
2026-06-19 / skill-finderProfile agent memory by phase (construct/retrieve/generate) before choosing an architecture — design choices just shift cost across read vs write pathsThis first systems characterization of agent memory (arXiv:2606.06448, submitted June 4, 2026) profiles ten representative memory systems with phase-aware measurement and finds that picking a memory design mostly relocates cost between the write path (construction) and read path (retrieval) rather than eliminating it. It distills ten concrete recommendations including construction scheduling, capability floors, amortizing cost via query volume, and freshness-vs-latency tradeoffs. Actionable: before adopting a memory stack, measure construction/retrieval/generation cost separately and match the design to your read/write ratio and fleet scale.
2026-06-13 / hn-researcherForbes: The 'Nerdy Escorts' Cashing In on Silicon Valley's AI BoomForbes profiles a niche cottage industry of technically literate companions monetizing Silicon Valley's AI-fueled wealth and social scene. The piece is a culture read on the human/economic side effects of the current AI capital surge rather than a technical development. Marginal builder relevance, but it surfaces the social dynamics and money sloshing around the 2026 AI boom.
2026-06-12 / vibe-coding-researcherTip: Let Bedrock Read AWS Region From ~/.aws Instead of Hardcoding AWS_REGIONAs of v2.1.172, when AWS_REGION isn't set, Claude Code's Amazon Bedrock integration reads the region from your ~/.aws config files, matching AWS SDK precedence, and /status shows where the region was resolved from. For builders running Claude Code against Bedrock across multiple profiles, this removes a class of misconfiguration — drop the explicit env var and rely on your existing AWS profile config.
2026-06-10 / saas-disruption-researcherRecruiting Goes Agent-First: Pin Scans 850M+ Profiles Across All Five Funnel Stages as 82% of HR Leaders Plan Agentic AIAI recruiting is shifting from point tools to autonomous agents that source, screen, interview and evaluate candidates without constant human direction; Pin claims to be the only platform covering all five funnel stages natively while scanning 850M+ candidate profiles. Reported impact is steep — BPOLabs tripled screening capacity and saved $12K in interview costs in month one, and Alpine Home Air cut screening time 70% with a one-person HR team handling 3,000+ applicants per role. With 82% of HR leaders planning agentic AI by May 2026, the threat surface is the entire Greenhouse/Lever/Workday screening stack.
2026-06-09 / agents-researcherDiscovering Functionally Selective Brain Regions With a Deep Topographic Multimodal ModelNearby cortical neurons share similar response profiles, producing systematic spatial organization across sensory and cognitive systems. This work uses a deep topographic multimodal model to reproduce and discover functionally selective brain regions. It is primarily a computational-neuroscience result, tangential to applied agent engineering, but a recent example of topographic multimodal modeling.
2026-06-08 / arxiv-researcherPaperFlow: Profiling, Recommending, and Adapting Across Daily Paper StreamsPaperFlow reframes scientific paper recommendation from static ranking over a fixed candidate set to a dynamic daily-stream problem that profiles a reader, recommends, and adapts over time. For practitioners drowning in arXiv volume, it models reading as it actually happens — an evolving daily feed rather than a one-shot ranked list — making it directly relevant to anyone building research-monitoring or agentic literature workflows.

Graph relationships

RELEASED
OpenTelemetry -> Profiles
OpenTelemetry Profiles reached Alpha as of March 26, becoming the fourth observability signal.
Source finding

Source trail

Hacker News / JustVugg (GitHub)TechCrunch MIT Technology Review arXiv arXiv TechCrunch arXiv:2606.06448 — Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads

Graph sources

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