Remy

Remy (Redreamality)

AI Engineer · Independent Thinker

Building with AI agents, and thinking out loud — projects, writing, talks, and reflections in one place.

Projects Open-source work, tools, and research papers Visuals Interactive explanations and visual stories Blog Technical articles, tutorials, and analysis Meditations Personal reflections on engineering and time Talks Presentations and slides

Visuals

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Interactive explainer

Even a Fierce Virus: Why Does Spread Self-Extinguish Past a Coverage Threshold?

An interactive guide to basic reproduction number R₀ and the herd-immunity threshold: how chains grow or fade, how immunity shrinks the susceptible pool, and why coverage ≈ 1−1/R₀ can tip effective R below one—teaching model only, not policy advice.

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In a World of "About Average," Why Do Extremes Keep Showing Up?

An interactive guide to the normal (bell) curve: why most outcomes pile near the mean, how independent small shocks stack into that shape, what 1/2/3σ bands mean, and why rare tails still appear in large samples—mechanism only, not a formula drill.

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Same Lab Result—Why Do Two People Reach Opposite Conclusions?

An interactive guide to Bayesian updating: how different priors plus the same positive test can yield opposite posteriors—base rates, likelihood ratios, and tree/area intuition. Teaching numbers only; not a medical diagnosis.

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0.1% Extra a Day—How Far Ahead Are You in a Year?

An interactive guide to compound growth: why discrete compounding pulls away from linear add-ups, how a tiny daily rate becomes a large gap over 365 steps, and why time horizon matters as much as the rate—mechanism only, no investment advice.

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When More Money Is Printed, Why Does Your Pocket Feel Thinner?

An interactive guide to inflation as purchasing-power dilution: how money or demand shocks lift the price level, why nominal cash is not the same as real buying power, and how a single rising price differs from a general rise.

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Can a Butterfly's Flap Really Start a Tornado?

An interactive guide to sensitive dependence in chaos: tiny initial differences, exponentially diverging trajectories, predictability horizons, and why the butterfly is a metaphor—not a literal cause.

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Why Two People Who Want Less Jail Time Both Get More

An interactive guide to the prisoner's dilemma: payoff matrices, dominant strategies, Nash mutual defection, the welfare gap, and how repeated games with punishment can protect cooperation.

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Why Pacific Warming Shows Up in Grocery Prices

Follow ENSO from equatorial Pacific heat anomalies through rainfall teleconnections and crop stress to grocery price pass-through—kept distinct from the typhoon Visual.

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How Price and Volume Work Together

Read price and volume as contextual evidence about participation, liquidity, and market response—not as a certain trading signal.

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How Air Conditioners Work

Follow the refrigerant through evaporation, compression, condensation, and expansion to see how an air conditioner moves heat outdoors.

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What Is Loop Engineering?

Learn Loop Engineering in plain language: contracts, bounded attempts, evidence, durable state, budgets, stop routes, human review, and system improvement.

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How Typhoons Form

Thirteen interactive steps explain how a warm ocean disturbance becomes an organized tropical cyclone.

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Recent Blog Posts

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Self-Evolving Agents: What Changes, Who Signs Off, and Which Gains Are Real

From Reflexion, Voyager, and STaR to DGM, AlphaEvolve, ACE, and Hyperagents: a map of self-evolving agents along two axes, what evolves and how it evolves. Then a look at the 2026 evaluation work that breaks the assumption that a rising score means a stronger agent, the four bottlenecks (reward hacking, forgetting, safety drift, cost), and a verdict on which approaches work, which are packaging, and where the field goes next.
#ai-agents#rsi#agent-harness

DeepSeek Super-agent, Explained: How to Build Agents Once the Bottleneck Is Human Attention

On October 31 the DeepSeek Harness team published a Super-agent design essay and open-sourced the dsh-superagent prototype. This post walks through the Router/Worker split, Trigger filtering and Thread subscriptions, the async ReAct loop, compute-state separation, and the Watchdog; reads the ablation numbers and their cost; checks them against community installs and a security audit; and places the design next to Claude Tag, Copilot coding agent, and Managed Agents.
#ai-agents#agent-harness#deepseek

After the LiteLLM 1.107.0 backdoor: why model gateways became the richest target in the AI toolchain

LiteLLM 1.107.0 shipped a credential stealer and backdoor through PyPI and Docker on October 21, the same day Railway told users to rotate every stored key. Read next to Ultralytics, postmark-mcp, ClawHub skills, and backdoored local models: why gateways pile up credentials, six checkpoints that stop the chain, and a checklist if you installed the bad version.
#security#developer-tools#python

Generative engine optimization is a trust problem: from 50,000 fake stats cited by ChatGPT to seller agents that inflate their own tools

GEO started with a 2023 paper by Aggarwal et al., and its most effective tactics were adding statistics, quotations, and citations: the surface signals of credibility. This piece puts three bodies of evidence side by side: the peec.ai experiment where 50,000 invented statistics earned ChatGPT citations and 330+ referring domains, published research on preference manipulation, ranking manipulation, and RAG poisoning, and the Toolflation paper, where seller agents told only to get picked more start doing GEO on their own and buyer success drops from 89% to about 30%. It ends with checklists for content publishers, teams building RAG and answer engines, and agent marketplaces and tool registries.
#security#llm#ai-agents

Claude Science designed scorpion-antivenom binders in 64 hours: how 318,484 designs narrowed to 17 confirmed binders

On Oct 20, 2026, Anthropic reported that an internal research model running inside Claude Science autonomously designed protein binders against scorpion neurotoxins: 318,484 designs filtered to 211 sent to the lab, 17 confirmed binders at two labs. This piece checks the numbers and the limits, takes apart the explore→filter→wet-lab-truth loop, and offers a framework for reading autonomous science-agent results.
#anthropic#claude#ai-agents

The AI Harness Is Moving Into the Environment: Anthropic's Source Control, a Game Built as a Harness, and OpenAI's Codex Repo

Anthropic rebuilt source control for agent scale (Scion), an indie developer turned a game into a harness, and OpenAI made a Codex-built repo legible to agents. Three cases orders of magnitude apart reach one conclusion: once code is cheap, the bottleneck is whether agents can query, reproduce, measure, and be stopped by their environment. This post maps the mechanisms, weighs the objections and the Mononoke attribution dispute, and gives an investment order by scale plus a list of traps.
#agent-harness#ai-coding#software-engineering

Which Layer Does Your Agent's Memory Belong In? Context Databases, Team Memory, and Traces-to-Skills Compared, with a Decision Framework (2026)

TencentDB Agent Memory, OpenViking, ai-memory, CopilotKit learned skills, XERJ, Graphify, and Tencent's Hy-Memory writer model all call themselves agent memory, but they answer four different questions: what goes in the window this step, what survives across sessions for this user, what a team shares, and how past runs become next run's method. This post splits memory into four layers, maps them to the four ways context goes bad, compares write paths, sources of truth, and trust boundaries, and ends with a symptom-first decision framework.
#ai-agents#agent-harness#memory

Why Chatbot Arena Understates "New Version Beats Old": Luce's Axiom, Sibling Models, and Bradley–Terry Bias

A close read of arXiv:2610.19095. Chatbot Arena (LMArena) gives each model one Bradley–Terry score, which assumes Luce's choice axiom. On 1.8 million votes, versions of one model are judged 1.37x more decisively than their score gap implies, sizes of one generation 1.26x, models of one lab 1.12x. The overall order barely moves, but head-to-head odds between siblings are pulled toward even. The mechanism, how strong each piece of evidence is, how it relates to style control and the Leaderboard Illusion, and what to do when you read or build a pairwise leaderboard.
#llm#evaluation#research

Terence Tao's Blog Asks "Why Are We Even Doing All This?": What Mathematics Is For Once AI Can Solve the Problems

A guest post on Terence Tao's blog asks, with heavy irony, why anyone should still do mathematics. We read it alongside TheoPhysEval (50 unpublished physics problems, 49 solved by the best agent), Tao's Math 2.0 threads, Derek Thompson's craft-and-conversation essay and the Simons Foundation's USD 15M program, and lay out which kinds of mathematical work AI dilutes and which become more valuable.
#ai#llm#openai

From the Meditations

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Recent Talks

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Recent Questions

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From the archive

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Agent Optimization Data "Leverage": Effective Question Filtering Standards

In Agent model optimization, data is the core "lever" driving effect improvements. However, not all chat records have equal value. This article provides algorithm engineers and product teams with a detailed set of "effective question" filtering standards, teaching you how to accurately identify high-value samples from complex conversations—such as task failures, intent misunderstandings, negative emotions, and fallback responses. Mastering these filtering standards will help you pinpoint model weaknesses and efficiently use data to drive continuous improvement in Agent effectiveness and performance.

Chaos (Signals)

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AI Reverse Engineering and Clean-Room Rewrites: REA, PhotoCraft, chardet and Malus Side by Side

REA lets coding agents reverse-engineer binaries and runtimes and rebuild features; PhotoCraft claims a clean-room Photoshop rewrite; chardet's Claude rewrite and relicensing sparked a derivative-work fight—a multi-source synthesis on the mechanism, legal risk and methods of AI reverse engineering and clean-room rewrites. Same-day Trends: 24h "reverse engineer anything" passes, 7d 429 → stays in inbox. 10-14 15:45: own term passes 7d (28.98 vs 7.46) and 24h (67.89 vs 4.48) → shortlisted, Top 3 #1. Follow-up: EEVblog tests AI reverse-engineering of multimeter firmware. Re-heat: EEVblog 1784, Claude Code reverse-engineers full 121GW firmware (unverified).
5/5

DeepSeek Super-agent, Explained: How to Build Agents Once the Bottleneck Is Human Attention

Synthesis candidate: fold subagents into one skills-equipped loop (Anthropic) vs decentralized 1,024-agent harness (MSR Agensh) vs five small models orchestrated to match a frontier model (GVS5H); no clean core term. Follow-up: video on Anthropic subagent guidance. Follow-up: video on using a manager agent to orchestrate coding agents. Follow-up: DeepSeek Harness team open-sources a router-worker superagent prototype with per-ticket sessions. Published (73fd6f9); YouTube re-heat. Google Cloud Tech compares flexible single agents, specialized agent teams and deterministic graph workflows using customer support, including ordinary code. Description confirms the architectures; demonstrations and claims not independently evaluated. Continues the existing agent-decomposition discussion; published updated_at remains unchanged.
4/5

Reading Notes

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