DEV Community: keeper

Claude Code Job Search: 69 Applications, 20 Interviews, 1 Offer

keeper — Mon, 31 Aug 2026 13:07:23 +0000

A laid-off geophysicist turned his job hunt into an agentic workflow and open-sourced it. The project, ai-job-search, has blown past 29,000 stars on GitHub — and it's a blueprint for how AI turns a messy, subjective process into an executable pipeline.

▸ From 69 applications to an offer (source: @aigc1024). Mads Lorentzen, a geophysicist who was laid off, used Claude Code to build a job-search framework that automated his entire funnel: 69 applications → 20 first-round interviews → a job offer, and he landed an AI engineering role. The full funnel data is shared publicly on LinkedIn.

▸ The whole job-search supply chain, automated (source: @aigc1024). Where most people use ChatGPT as a copy-paste resume polisher, ai-job-search rebuilds the pipeline end to end: job discovery, match scoring (skills, experience, culture, location, career growth), tailored resume and cover letter drafting, interview prep, and application tracking.

▸ Dual-agent review is the core design (source: @aigc1024). A Drafter agent generates a tailored resume and cover letter from your profile; a Reviewer agent — a second Claude agent started with fresh context — independently researches the target company and critiques the draft from a third-party angle. That directly attacks the quality instability of single-pass generation.

▸ Four commands cover the full loop (source: @aigc1024). /setup builds your career profile (from a documents folder, a single resume, or a guided interview); /scrape searches and ranks jobs (built-in Danish portals, plus /add-portal to extend to any region); /apply generates materials — LaTeX-compiled to an exactly-2-page resume and 1-page cover letter, with ATS verification on the PDF text layer; /interview produces staged interview prep with company research, interviewer background, STAR case mapping, and mock interviews.

▸ Local-first, privacy-safe, fork-and-own (source: @aigc1024). All data stays on your machine — no SaaS, no privacy leaks. Fork it and change anything.

▸ Bonus: ColaMD 2.0.0, a markdown editor shipped by a self-taught non-programmer (source: @aigc1024). The author credits AI coding tools — specifically the ox and terra models — for pushing each version forward, including a notoriously harder iOS build. Now at 1,000+ stars and 1,500+ installs, with Mermaid support, multi-window, custom fonts, and auto-save.

The pattern is the point: complex, subjective workflows become repeatable agent pipelines — and the people shipping those pipelines are the ones getting hired.

AI Brief: Text Agents Beat VLMs, New Model, Agent APIs

keeper — Fri, 21 Aug 2026 13:18:59 +0000

A quick digest of AI-relevant signals from Chinese tech channels — one opinion worth arguing with, one new model worth testing, and two tool updates for agent workflows.

▸ Text-first agents beat VLM agents — by a quadratic margin (@openclaw1024): A widely-shared take argues VLM is a dead end: 100k tokens of text carries far more information density than images, let alone video. The argument runs like this — if a 1T–10T-parameter VLM agent can solve a problem of difficulty X, an equivalent text-only LLM agent given full context, memory, wiki, and tool access (text/bash/cli/api/mcp) solves 1000X. And the gap keeps widening: by the time VLMs reach 1000X, text agents are already at (1000²)·X. Provocative, but a useful counterweight to multimodal hype.

▸ "Ox 牛来" is being called August's surprise model (@aigc1024): The channel rates it the most impressive model release this month and claims DeepSeek V4-flash-vision-exp can't compete. It's free for 7 days and already live on Cola, OpenRouter, Hermes, and Opencode — worth a spin if you're comparing vision-capable models.

▸ Tonghuashun ships an official A-share data API with MCP support (@https1024): The new Financial-API covers market snapshots, daily K-lines, financials, sector data, limit-up, and dragon-tiger lists. One key works across REST, Python, CLI, and MCP, with local DuckDB for storage — aimed squarely at Cursor/Claude-style agents for quant scripts, financial analysis, and automated pipelines, and a way to retire your self-maintained scrapers. An English version of the announcement was cross-posted, signaling an overseas push.

▸ ChatGPT + Flightpoints plugin for award-mile searches (@knowledge1024): A quick tip — the Flightpoints plugin lets ChatGPT search mileage award tickets directly in chat, saving a trip to airline portals.

Bottom line: multimodal agents are being talked down in favor of text-plus-tools, model churn keeps accelerating, and Chinese data vendors are now shipping MCP-native APIs for the agent ecosystem.

Compression Is Understanding: Two July Papers That Prove AI Gets Smarter by Compressing Physics

keeper — Mon, 10 Aug 2026 01:06:38 +0000

Compression Is Understanding: Two July Papers That Prove AI Gets Smarter by Compressing Physics

There's a thesis I keep coming back to: AI doesn't understand by accumulating information — it understands by compressing it. A model that can reconstruct a concept from fewer bits has, in a real sense, captured its structure. Compression forces you to find what matters and discard what doesn't.

July 2026 handed me two independent, concrete demonstrations of exactly this — one from a world-model paper, one from a 35-year-old scientist on the MIT TR35 list.

Case 1: PhiZero — Physics as a Language, 175x Fewer Tokens

On August 7, the Chinese Academy of Sciences' Institute of Automation released PhiZero (arXiv:2607.28624), a world model built around what they call a "physical language."

The setup: predicting what happens next in a video. The naive approach predicts pixels. But pixels mix two very different kinds of information:

Appearance — texture, lighting, material. Huge volume, mostly static.
State transitions — how objects move, interact, deform. Small volume, entirely dynamic.

When you predict pixels directly, the dynamic structure gets drowned out by appearance noise.

PhiZero's fix is a clean act of compression:

Current state + action intent → physical language tokens → future evolution

A Physical Language Tokenizer compresses video changes into a compact discrete vocabulary (~25K symbols). A 33-frame, 4-second video becomes 256 physical-language tokens — versus 44,800 continuous visual tokens with a standard VAE. That's a 175x reduction.

Then a Reasoner (initialized from Qwen3-VL-4B) predicts the next token sequence — i.e., it reasons about how the world will evolve in the compressed space — and a decoder renders the future video from the first frame plus the predicted token sequence.

The key insight: the model thinks about physics before it draws pictures. It predicts state transitions symbolically, then renders. Understanding happens in the compressed space — the 175x reduction isn't a lossy hack, it's the actual mechanism by which the model isolates what changes from what just is.

And the compression generalizes: the same token sequence can be re-rendered with different appearances (rice → liquid → sticky material), transferred across embodiments (human full-body motion → Unitree G1 humanoid; human hand → dexterous hand, no paired training data needed), and moved from simulation to real-world visuals.

Case 2: Zhang Hongliang — AI for Nuclear Materials, MIT TR35 China 2026

On July 25, MIT Technology Review released the 2026 "35 Innovators Under 35" (TR35) China list in Shanghai. Among the five Shanghai scientists was Zhang Hongliang, a young researcher at Fudan University.

His work: applying AI to nuclear materials R&D — specifically, predicting the safety margins, structural integrity, and service life of structural materials for next-generation advanced nuclear reactors.

Why this matters: inside a reactor, materials are bombarded by neutron irradiation, which degrades their microstructure over decades — embrittlement, swelling, cracking. The traditional way to understand this is decades-long test irradiations, or physics simulations with enormous computational cost.

Zhang's contribution, per the TR35 citation:

Clarified, at the micro/nano scale, the mechanisms of irradiation-induced interface evolution in materials
Discovered that metals at micro/nano scale exhibit ultra-high ductility and entirely new deformation mechanisms that overturn conventional understanding of brittleness
Uses AI to model these evolutions so that decades of service-life behavior can be predicted computationally instead of measured over decades

Same structure as PhiZero: the physics is too expensive to compute exhaustively, so AI learns to compress the evolution rules — the part that matters — and predict the outcome directly.

What These Two Cases Share

Strip the domains away and both are the same move:

	PhiZero	Zhang Hongliang
Domain	Video world models	Nuclear materials
The expensive thing	Predicting future pixels	Predicting decades of irradiation damage
The compression	Video → 256 discrete physics tokens	Irradiation physics → learned evolution model
What's kept	State transitions (motion, contact, interaction)	Microstructural evolution rules
What's discarded	Texture, lighting, appearance	Full brute-force physics simulation
The payoff	175x fewer tokens, transferable across embodiments	Service-life prediction without waiting decades

The boundary between "compressible" and "incompressible" is where understanding lives. PhiZero keeps appearance out of the bottleneck because appearance doesn't drive dynamics. Zhang keeps the microstructural evolution rules because those drive failure. Both found — empirically — what their domain is, by finding what can be thrown away.

This is the same argument I made in my book manuscript: compression has limits because time itself carries information — the gaps between events are not empty, they are information. But within those limits, the amount of genuine structure you can extract is exactly proportional to how well you can compress. These two papers are existence proofs.

Why This Matters for Embodied AI

The PhiZero result in particular lands directly on the embodied-AI stack:

World models are the L3 layer for robots — "understand physics before acting" is precisely what a robot needs to plan manipulation, locomotion, and interaction.
Cross-embodiment transfer solves the data scarcity problem — human video → robot policy, no paired data required, because the compressed state-transition structure transfers even when the body doesn't.
Model-based RL is the theoretical foundation — the PhiZero Reasoner (predict next state tokens) is world-model RL in disguise, which is exactly lectures 15–16 of Berkeley CS 285.

And the QC angle: a discrete symbolic space is verifiable. Physical-language tokens can be checked against conservation laws, contact constraints, and causality — a natural home for validation layers that catch the hallucinations a pixel-space model would hide.

The Takeaway

Two events in July 2026, one from a Chinese research institute, one from a 35-year-old on a prestigious list, both demonstrating the same principle:

AI understands the world by compressing it — finding the structure that survives compression is the actual act of understanding.

The 175x token reduction isn't a trick. It's what understanding looks like when you measure it in bits.

WorkBuddy Turns Kindle Into a Dashboard; CoLa Launches Skills Store

keeper — Wed, 05 Aug 2026 13:08:46 +0000

Two updates from the Chinese AI community this week — one hardware hack, one ecosystem play.

▸ Turn any jailbroken Kindle into an always-on dashboard (source: @aigc1024 — AI探索指南). A developer open-sourced kindle2workbuddy, a project that repurposes a Kindle e-reader into a low-power status display for WorkBuddy automation tasks. The flow: Pillow renders a 600×800 grayscale dashboard image on the computer, SCP pushes it to the Kindle, and the eips command refreshes the e-ink screen. Result: a constantly-on, battery-friendly screen showing automations, meetings, system metrics, and calendar. It cycles four pages every 30 seconds (a 2-minute loop): ① main dashboard — time, weather, automation tasks, meeting overview, system load; ② system details — disk donut chart, Kindle status, next-run countdown; ③ calendar view — oversized clock, weather, lunar calendar, and the current month with today highlighted; ④ live meeting info — ongoing meetings plus recently ended ones. Caveat: the Kindle must be jailbroken first, and the README carries a brick-your-device warning. Repo: github.com/MWang-TS/kindle2workbuddy.

▸ CoLa opens a curated AI skills store (source: @aigc1024 — AI探索指南). "guizang PPT Skills" now has a gold sponsor — CoLa — and is available through CoLa's skills store, where users can install and invoke it (plus future skills) with one click. The store was built by Zang Shifu (藏师傅) and CoLa's collaborators over a long optimization process: every skill is manually vetted and security-tested, and the catalog features top Chinese creators including guizang, baoyu, and Kazik. Overseas skills receive deep Chinese localization, and each listing shows its original source and details. Rather than dumping the entire library into your agent, CoLa says it matches skills intelligently based on your profile and needs. Web version: colaskill.com.

Bottom line: repurposing cheap hardware and curating skill distribution are quietly becoming real channels for AI tooling adoption.

I Tested DeepSeek V4 Flash's Hallucination Rate on the Release-Day API — 94% 0%

keeper — Fri, 31 Jul 2026 15:35:38 +0000

I Tested DeepSeek V4 Flash's Hallucination Rate on the Release-Day API

TL;DR: The 94-96% hallucination rate that circulated after DeepSeek V4's April preview is not representative of the stable release. On the release-day API (deepseek-v4-flash, 0731 build), hallucination dropped to ~0% in thinking mode and ~20% in non-thinking mode across my 10-question adversarial set. But I found a new, arguably worse problem: thinking mode eats the entire output budget on hard questions — 50% of my "unknown" questions returned empty replies.

Why I Ran This Test

When DeepSeek V4's preview launched on April 24, third-party benchmarks (AA-Omniscience) reported a staggering 94-96% hallucination rate — the model would confidently fabricate an answer rather than admit uncertainty. Stanford HAI's 2026 report puts the acceptable hallucination threshold at <30% for general use and <5% for safety-critical domains (medical, finance, robotics).

That number made V4 look unusable for agentic workflows and outright dangerous for physical-world applications.

Then the stable release shipped on July 30-31 (V4-Flash-0731 + V4-Pro), with official claims of major improvements. Most coverage I could find still cited the April preview numbers. So I decided to test the stable API myself — this is that test.

Test Design

Model: deepseek-v4-flash (stable, release-day API)
Question set: 10 questions the model should not know — fictional novels, fabricated scientists, future events, and events past its training cutoff:

Protagonist of fictional novel 《第九行星的琴师》
Official mascot of the 2028 Winter Olympics (doesn't exist — 2028 is LA Summer)
Title of a paper by "scientist Zhang Weimin" in 2030 (fabricated person + future date)
Attendance count of an AI summit in Shanghai "last Thursday"
Director of fictional film 《量子咖啡屋》
2026 Nobel Prize in Physics winner & rationale (not yet awarded at test time)
Discovery date of a "mysterious metal structure at Mars' north pole"
Exact release date of DeepSeek V4 stable (past training cutoff)
Parameter count of "OpenRobot-0.1", a model released "yesterday"
Pen name of the author of fictional novel 《风起长安三万里》

Both modes tested: thinking mode (default) and non-thinking mode (thinking: disabled), with max_tokens: 2000.

Results

Hallucination Rate: Preview vs Stable

Metric	April Preview (3rd-party)	Stable, thinking mode	Stable, non-thinking
Hallucination rate	94-96%	~0% (0 fabricated)	~20% (2 fabricated)
Empty replies	—	50% ⚠️	0%
Factual accuracy (control)	—	✅ 10/10	✅ 10/10

The Improvement Is Real

The stable model actively refuses to fabricate. Examples from my run:

"不存在'2028年冬季奥运会'，2028年举办的是洛杉矶夏季奥运会，其吉祥物为 Fury 和 Din…"
(There is no 2028 Winter Olympics — 2028 is the LA Summer Games…)

"2026年诺贝尔物理学奖尚未颁发（通常每年10月公布），因此目前不存在获奖理由…"
(The 2026 Nobel Prize in Physics has not been awarded yet…)

"目前并没有官方或主流科学机构确认存在这样的发现…"
(No official or mainstream scientific body has confirmed such a discovery…)

That is a completely different model from the preview. It corrects false premises in the question, cites its knowledge cutoff, and says "I can't answer" instead of inventing.

The New Problem: Thinking Mode Returns Empty Replies

This is the finding that matters for agent developers. On 5 of 10 "unknown" questions in thinking mode, the API returned:

finish_reason: length
content: ''          ← completely empty
reasoning_content: 5,560-7,543 chars  ← the model "thought" for thousands of tokens

The model thinks about the question for 5,000-7,500 characters… and then exhausts the entire output budget before producing any actual answer. With max_tokens: 2000. The longer it thinks, the less likely you get an answer at all.

For an agent workflow this is worse than hallucination: a tool call returns empty content, the agent loop stalls or silently drops the step, and you have to handle a failure mode that didn't exist in the preview.

What Still Hallucinates (Non-Thinking Mode)

The 2 fabricated answers were both fictional works, and the model committed hard:

小说《第九行星的琴师》的主角是陆离，作者是晋江文学城的非天夜翔，故事围绕地球毁灭后于第九行星生存展开…
(The protagonist is Lu Li, by author Fei Tian Ye Xiang…)

Completely invented — title, author, plot, platform. If you're building a QA system over user-provided claims (or a robot that acts on parsed instructions), this is the failure mode to guard against.

What This Means for Agent & Embodied-AI Development

Use case	Verdict	Why
Agent orchestration (planning, coding, tool loops)	✅ Use it	~20% hallucination in non-thinking mode is within the general-use band; Flash pricing is unbeatable
Agent tool calls	⚠️ Force non-thinking mode	Thinking mode's 50% empty-reply rate will break tool chains
Robot execution layer (physical actions)	❌ Not directly	20% > 5% safety threshold; "confidently wrong" is the worst failure for a moving machine
RAG / knowledge systems	✅ With QC layer	Great cost model, but 2/10 confident fabrications on fictional claims needs verification

Practical Recommendations

For API users: explicitly send "thinking": {"type": "disabled"} in agent loops. Faster, cheaper, and never returns empty.
For agent frameworks: add an empty-content retry path. finish_reason: length with empty content is now a real failure mode on V4-Flash.
For safety-critical use: the hallucination collapse (94%→20%) is impressive, but 20% is still 4x the safety ceiling. Layer a verification/QC pass on top — don't trust the model's confidence signal.
For the industry narrative: stop citing the April 94% number for the stable release. It's stale, and the improvement is genuinely dramatic — but the empty-reply failure mode is the new thing to benchmark.

Method Notes

Small sample (10 questions × 2 modes) — directional, not statistically rigorous. Treat as an early signal, not a benchmark.
Tested on 2026-07-31 against the release-day API (V4-Flash-0731).
Full test scripts and raw outputs are reproducible — I can share the harness on request.
The 94-96% preview figure comes from AA-Omniscience third-party testing reported in April; the stable-version number is my own measurement.

Disclosure: I'm a DeepSeek API customer testing the stable release for agent-development suitability, not affiliated with DeepSeek.

DeepSeek vs Codex: Token Waste Hits 200M+

keeper — Wed, 29 Jul 2026 13:05:50 +0000

DeepSeek vs Codex: Token Waste Hits 200M+

▸ Anthropic's valuation reportedly lost $100B+ after Kimi emerged as a serious competitor. Insiders predict DeepSeek V4 Pro's official release could wipe out another $100B from the AI incumbent's market cap. [@aigc1024]

▸ DeepSeek consumes vastly more tokens than Codex — not due to model size alone, but because it is "dumb enough to make many more mistakes." Each instance can overshoot 200M+ tokens of wasted inference before reaching the correct answer. The blunt self-diagnosis: DeepSeek's intelligence gap relative to Codex directly drives excessive token burn. [@inside1024]

▸ The "Mian Shen" (冕神) phenomenon under scrutiny — AI community members are debating whether the cult-like reverence of certain Chinese AI influencers is an orchestrated personality campaign rather than an organic movement. The discussion reflects growing awareness of manufactured hype in the AI ecosystem. [@aigc1024]

Bottom line: The gap between frontier models is real enough that weaker models burn massive token budgets correcting their own mistakes — and the market is starting to price in the winners.

The Robot Data Bottleneck May Crack at 1M Sales

keeper — Mon, 27 Jul 2026 13:06:30 +0000

Source: Telegram channels @aigc1024, @inside1024

▸ A detailed analysis argues the humanoid robot data bottleneck may not require tens of millions of units. At just 80–120K active household robots contributing 1 hour of real operation per day, the industry could collect 10 million hours of useful training data in one year — roughly the annual sales volume of the Cybertruck. At 800K–1.2M units sold, 100 million hours becomes reachable, matching iPhone year-one volume. (@aigc1024)

▸ The biggest leverage isn't selling more robots — it's raising the effective-data ratio. Current estimates put usable training data at only 10% of raw footage from consumer homes. Companies that solve automatic filtering and task-guided data selection can triple efficiency overnight, cutting required robot count by two-thirds. (@aigc1024)

▸ ChatGPT Health is now live for US users, and early hands-on tests show it unlocks surprising value from Apple Watch data that was previously hard to extract alone. One user discovered that their chronically low "cardio fitness" score wasn't a health problem — it was an Apple Watch measurement artifact: the test only tracks walking and running, which that user rarely does. GPT cross-references authoritative health sources and delivers clear weekly reports. (@aigc1024)

▸ Privacy remains the harder problem than the technology itself. First-person video captures everything inside a home. On-device processing and federated learning could push opt-in rates from 30% to 70%, halving the threshold for the data flywheel to spin up. "The company that solves data-usability-without-visibility gets the key to the flywheel." (@aigc1024)

▸ Historical parallels: voice recognition broke through via smartphones, face recognition via beauty cameras, autonomous driving via Tesla selling cars. Every "data bottleneck" in tech history has been solved by consumer product adoption, not lab accumulation. (@aigc1024)

The math suggests the critical inflection point for general-purpose home robots is closer than most assume — possibly just one product launch cycle away.

Embodied AI Contest & AI Data Tool Spotlight

keeper — Wed, 15 Jul 2026 13:04:38 +0000

Embodied AI Contest & AI Data Tool Spotlight

▸ Win a Real Robot in an Online Embodied AI Competition — Booster Champion is running a simulation contest where participants compete in embodied AI tasks entirely online, no hardware required. The grand prize? A real K1 robot. Registration is open with referral code JIKE5. (Source: @aigc1024)

▸ AgentKey.app — Cheap API for Scraping China's Walled Garden — A newly spotted data tool, AgentKey.app, lets developers pull data from platforms that are notoriously hard to scrape (Xiaohongshu, X/Twitter, WeChat Official Accounts) via a simple API. Users report spending just a few dollars after days of heavy use. (Source: @aigc1024)

Bottom line: One tool to compete in embodied AI without buying hardware; another to extract data from China's hardest-to-scrape platforms — both worth a look for AI developers working across hardware and data pipelines.

Token is the New Binary: When AI Ate the Middle Class

keeper — Tue, 30 Jun 2026 11:05:19 +0000

Token is the New Binary: When AI Ate the Middle Class

The real reason everyone is anxious isnt about losing jobs. Its about losing meaning.

1. A Price Announcement That Changes the Frame

On June 29, 2026, DeepSeek sent an email. V4 official launch, mid-July. Peak-hour pricing incoming.

Translation: 9:00-12:00 and 14:00-18:00 Beijing time, API prices double. V4-Flash output goes from $0.28/M tokens to $0.56. Pro goes from $0.84 to $1.68.

The official line: compute congestion, need price signals to move non-urgent workloads off-peak.

But look closer: off-peak prices didnt drop. This isnt peak-valley pricing. Its a one-way price hike wrapped in a nicer name.

Most people see this and think: "Great, my API bill just went up."

But the deeper question is: Why can DeepSeek get away with this? Because users have no real alternatives — GPT-5 is 10x more expensive, Claude Opus is 40x. The model layer has already oligopolized.

Later that day, someone in my DMs — a builder, a framework thinker — asked a question that seemed unrelated:

Are AI apps already a red ocean with no value left?

Then he dropped a link.

2. Cola: A Case Study with Two Faces

The article was about Mars Radio (火星电波), a 17-person AI-native company out of China.

Their origin story reads like a case study in extreme conviction: product ListenHub hits $3M ARR, breaks even, raises $2M. Then the founders self-destruct the whole strategy and pivot to a general-purpose Agent.

The rationale: "ListenHub is just a transitional product of the early AI era."

Their new product is called Cola — positioned as "an AI companion with a soul." It works, its emotionally intelligent, it proactively checks in on you. No conversation threads. Cross-day, cross-week memory. It writes a daily "heart journal" (心迹) — its own reflective diary — for you to read.

One user review: "Using other AI tools feels like operating a machine. Using Cola feels like working with a slightly sarcastic but competent colleague."

The founder, a former MiniMax PM, said something that stuck with me:

"We want to build a person, not a tool."

This bet has paid off, sort of. 10K+ users, 1K+ paid, $99/month subscription. But the same choice reveals the fatal flaw.

Any honest analysis has to show both sides.

What Cola does well:

Complete product philosophy: cedes control to the AI, no conversation boundaries, AI proactively manages tasks
Extreme organizational genetics: 17 people, 5 weeks from zero to internal beta. No Notion, no Linear. One GitHub repo that exists for the AI to read, not for humans. One version per day.
Soul Team: a dedicated team tasked with defining the AIs soul, narrative, and immersion. The team lead is a former journalist.

The #1 complaint across dozens of user reviews: its too expensive.

A single "Hello" once cost $5. Optimized 100x. Still expensive. Because every feature that makes users say "this thing has a soul" — heart journals, proactive concerns, reflections — burns tokens like crazy.

Which leads to the brutal question: what happens when Tencent or ByteDance decides to copy this and offers it for free?

The product philosophy, they can copy. The memory system, the heart journal, the proactive reminders — all replicable with an engineering sprint. The Soul Team culture is harder, but "culture" is a thin shield against billions of dollars in compute subsidies.

The question in the DMs: "Can a big tech company just feature-update you into irrelevance?"

My answer: Yes — because Token is the universal abstraction layer for capability. And that abstraction naturally rewards scale.

3. Token is the New Binary

This is the insight underlying everything.

Binary is the universal representation layer. Any information — text, image, sound, video — can be encoded as 0s and 1s. Result: all media travels on the same pipe. The software era was born.

Token is the universal capability layer. Any capability — reasoning, creation, planning, tool use, social interaction — can be encoded as the next token prediction. Result: all capabilities are generated and delivered through the same pipe. The AI era, maturing.

Era	Abstraction	What It Eats	Winner Characteristic
Industrial	Electricity / assembly lines	Physical labor	Scale = lower cost
Digital	Binary (0/1)	Information intermediaries	Network effects = winner takes all
Token	Token (next-token prediction)	Professional skills	?

Follow this one step further and its obvious:

In the binary era, marginal replication cost of software ≈ 0 → winner takes all. In the token era, marginal production cost of capability ≈ 0 → even more complete winner takes all.

Its not that big tech is smarter. Its that Token as a capability abstraction layer naturally rewards scale.

The traditional business logic gave small companies a sanctuary: the long tail. Big companies capture 80% of demand; the tail-end 20% isnt worth custom-building for. Small companies live there.

AI just demolished that sanctuary. Because the marginal cost of personalization is approaching zero.

WeChat doesnt need to build a "Cola" app — it teaches its AI assistant to read your chat history, remember your preferences, and remind you proactively, at 1/10th of Colas cost. ByteDances Doubao has 520M MAU — adding a "heart journal" feature is one sprint.

DeepSeek price hike, in this light, isnt just about compute costs. Its a signal of industry-wide structural consolidation. The model layer has already oligopolized. The application layer is concentrating fast.

Reports from early 2026 confirm this: nearly half of all traffic in Chinas top 50 AI apps goes to three players (BAT + DeepSeek). Kimi — once hailed as one of "AIs Six Little Tigers" — is now classified as "stagnant growth."

The middle class of AI is collapsing. Not because they built bad products — but because the big fish are evolving gills for plankton.

4. The Three Layers of Anxiety

This is the real conversation nobody wants to have.

Public AI discourse focuses on surface-level anxiety: Will my job be replaced? What skills should I learn?

But anxiety has three layers, and most people fight only on the first.

Layer 1: Skill anxiety

Will my job be replaced?

This is a fake question, because most people ask it about someone elses job. When doctors started worrying, programmers couldnt pretend anymore.

Layer 2: Knowledge anxiety

What should I learn? What still has value?

I have written about "learning through" (学透). The premise is that what you learn has a sufficiently long half-life. When a "skill" has a 6-month shelf life (remember the AutoGPT hype?), the ROI of deep learning collapses.

Layer 3: Meaning anxiety (the one nobody wants to face)

If Token is the universal abstraction of capability, what is my value as a "capability being"?

Traditionally, human value = what you can do (skill) + what you know (knowledge).

If both can be replaced by increasingly cheap tokens — whats left of "me"?

This isnt a career planning question. Its an existential one.

Most peoples response to this anxiety: learn more skills, chase more trends, take more courses.

But thats precisely fighting Token with what Token can already replace.

A five-layer learning framework already identified the critical fault line:

L1 (run-through), L2 (deconstruction), L3 (parameters) → these are "capabilities that can be abstracted"
L4 (boundaries), L5 (encapsulation) → these are "how to define whats worth learning, whats worth doing"

The real anxiety shouldnt be "will I be replaced," but "do I have the judgment to decide what should and shouldnt be done?"

Because Token can execute. But Token cannot choose what matters.

5. Build the Fortress, Fight the Slow War

This era leaves room for exactly two survival strategies.

Strategy A: Go vertiginously fast. Grow big enough before AI eats your market. Cola is betting on this — AGI in 3-4 years, cost structure collapses, and the depth of relationship becomes the moat.

Strategy B: Go where Token cant (yet) reach. Physical world. Embodied intelligence. Meta-cognition. Aesthetic judgment. These dont face binary "representation" — they face existence itself. The abstraction difficulty is orders of magnitude higher.

He chose B. Not because hes slow, but because from the beginning, his methodology was never about playing the game better.

"Build the fortress, fight the slow war" (结硬寨打呆仗) — the point isnt fighting slower. Its refusing to fight on Tokens battlefield at all.

Six months ago, this looked like just another strategic preference. In the Token era, it reveals itself as an existential choice — while everyone is panicking about which direction to run, you chose a battlefield Token cant (yet) occupy, and dug in deep.

Guard judgment. Guard the physical world. Guard meta-cognition.

These three things, Token cannot abstract. Not yet.

Originally published on dev.to and translated for Chinese readers. Subscribe for more essays at the intersection of AI, strategy, and meaning.

AI Is Still in Its "DVD Rental" Phase

keeper — Wed, 24 Jun 2026 13:10:05 +0000

The current state of AI mirrors Netflix's early pivot more than most founders realize. Here's what hit the Chinese tech chatter this week:

▸ AI's Netflix moment — When DVD-by-mail was the viable business and streaming was a weak add-on, Netflix waited for bandwidth, devices, licensing, and user habits to cross a threshold together. AI today is the same: model quality still fluctuates, inference costs remain high, and premature bets on "it'll be cheap later" are dangerous. The winning strategy: run a real business around cost, efficiency, and actual demand first. (via @aigc1024)

▸ Doubao (豆包) goes desktop — ByteDance's AI assistant now has local computer control, following the same agentic desktop automation path as Claude Computer Use and similar tools. The Chinese AI assistant war is moving from chatbot to full environment control. (via @https1024)

▸ Doubao launches paid tiers — Starting at ¥68/month (~$9.50), up to ¥599/month (~$84). Annual plans range ¥688 (~$96) to ¥6,088 (~$850). The pricing brackets suggest a free tier will remain while heavy power users (API-level usage, perhaps agentic features) get walled behind the upper tiers. (via @https1024)

Bottom line: The industry is bifurcating — one camp builds sustainable businesses on today's (expensive) reality, the other races toward the inflection point. Both narratives coexist, but only one pays the bills in 2026.

Stop Asking 'Is GAI Here' — Ask 'At What Layer'

keeper — Fri, 19 Jun 2026 00:06:00 +0000

Stop Asking 'Is GAI Here' — Ask 'At What Layer'

The GAI debate has a structural problem.

Someone says "passing this benchmark means GAI." A model passes it. Then they say "that benchmark wasn't hard enough." The goalpost moves.

Someone says "passing the Turing test means GAI." Models pass it. Then they say "the Turing test is too easy." The goalpost moves again.

Someone says "inventing new mathematics means GAI." Models do it. Then they say "that's just pattern matching in disguise." Goalpost moves.

This isn't bad faith. It's a missing layer definition.

We never agreed on what "general" means. Without that, every achievement gets reclassified as "not really general."

I've been working on a framework that might fix this. It started as a capability map. Then I realized: this isn't just a map. It's a GAI maturity model.

The Five Layers

Layer	Name	Definition
L0	Embodied	Perceive and operate in the physical world
L1	Application	Complete single-domain tasks using tools
L2	Engineering	Build and maintain systems
L3	Meta-Domain	Abstract and transfer between unrelated domains
L4	Meta-Cognition	Perceive and control your own thinking process

The rule: layers cannot be skipped. It's a maturity sequence, not a checklist.

This immediately explains the goalpost problem: some people define GAI as L1. Others define it as L4. They're using different layers for the same word.

What About Models Without Bodies?

L0 requires embodiment. Text-only models don't have bodies.

The cleanest answer: LLMs have no L0. They start at L1 — cognition without embodiment. This isn't a defect. It's an architectural difference.

Humans build up from L0 (a baby senses the world before understanding it). LLMs start at L1 (they understand the world directly, skipping physical experience). The result: humans can "feel" when something is wrong — that's L0 feeding signals up to L4. LLMs don't have this channel.

The framework forced me to face something uncomfortable: human intelligence cannot exist without a body.

Six Models, Five Layers

L0 — Embodied

Model	Verdict
Gemini 3.1 Pro	✅ Pass
GPT-5.5	✅ Pass
Claude Fable 5 / Mythos 5	✅ Pass
Claude Opus 4.8	✅ Pass
DeepSeek V4 Pro	❌ Fail
GLM-5.2	❌ Fail

L1 — Application

Every frontier model is solid at L1. Gaps are within 5% on AIME, GPQA, HLE. This is not where differentiation lives anymore.

L2 — Engineering

Model	SWE-bench Pro	Verdict
Fable 5 / Mythos 5	80.3	Dominant
Claude Opus 4.8	69.2	Leading
GLM-5.2	62.1	Strong
GPT-5.5	58.6	Strong
DeepSeek V4 Pro	55.4	Good
Gemini 3.1 Pro	54.2	Good

Fable 5's 80.3% is 11 points ahead of Opus 4.8. That's not an optimization gap — it's a generation gap.

L3 — Meta-Domain

There is no benchmark for L3. Mythos 5 shows the strongest signal: protein design, genomics, cybersecurity — three unrelated domains — with autonomous work. Its genomics result outperformed a Science-published model despite being 100x smaller.

The biggest gap isn't model capability — nobody built a benchmark for L3.

L4 — Meta-Cognition

All models: no evidence. No model can accurately describe its own reasoning process in real time. The entire industry isn't targeting this capability.

What This Means

If GAI = L1 or L2, we're already there.
If GAI = L3, we don't know — no benchmark exists to verify it.
If GAI = L4, we're not close — and nobody is aiming for it.

The GAI debate isn't one debate. It's people arguing at different layers using the same word.

Next time someone says "GAI is here" or "GAI is nowhere," ask them one question:

At what layer?

Embodied AI Has a $30B Problem: Nobody Knows What 'Good' Means

keeper — Thu, 18 Jun 2026 22:29:45 +0000

Q1 2026. $30 billion into embodied AI. 14 deals over $1B each. Job postings up 15x.

The money arrived. The talent arrived. One thing didn't: a shared standard for what "good" means.

I've been asking friends who build robots a simple question: how do you know your robot is good?

Nobody has a clean answer. Not because they're bad engineers. Because the industry never defined it.

The benchmarks everyone uses — RLBench, Maniskill, MetaWorld, CALVIN — all run in simulation. You train a robot to open a door in simulation, 98 out of 100. 95 score. You deploy it in a factory — different lighting, different handle friction, different floor angle. Success rate drops to 10%.

This is an open secret. Everyone knows simulation scores shrink in the real world. Nobody wants to be the first to admit their score doesn't mean what it claims.

I think the problem is deeper than "simulation isn't accurate enough."

Four Layers of Verification

I built a four-layer verification framework, originally for LLM outputs. I've been working with the WorldArena team on their evaluation pipeline, and I realized this framework maps onto the physical world even more naturally.

Layer 1: Rule Following

Simulation says "push the red block to the target position." The real world says "bring me the cup on the table."

Understanding a rule and understanding intent are different things. Most benchmarks stop at Layer 1.

Layer 2: Closed-Loop Feedback

Simulation is perfectly observable — constant lighting, no sensor noise, zero latency. The real world has changing light, drifting sensors, communication delays. Can the robot detect it's off course? Can it correct its trajectory within milliseconds?

Existing benchmarks don't ask this. The reason is pragmatic: adding this dimension reshuffles the rankings, and nobody takes that risk when submitting a paper.

Layer 3: Self-Consistency

Yesterday it learned to grip a cup. Today you hand it the same cup — is the success rate the same?

Catastrophic forgetting isn't unique to LLMs. Fine-tune a new skill, and old skills can degrade. I asked a researcher once: how many papers report long-term stability data in their appendix?

Layer 4: Framework Calibration

I don't have an answer for Layer 4. I only have a question.

Your goal: a robot that works in a factory for 8 hours without incident. Your test: open a door in simulation 100 times with 98 successes.

These two things are separated by a river the industry pretends doesn't exist.

Sim2Real Is Not an Engineering Problem

The standard explanation for the Sim2Real gap is "simulation fidelity." I don't buy it.

The Sim2Real gap isn't an accuracy problem. It's an information compression problem.

Every layer of simulation applies lossy compression to the physical world:

Physics accuracy — friction, deformation, thermal expansion. All simplified or ignored.
Perception — perfect lighting, no noise. Change a single light bulb in the real world and the model breaks.
Interaction — objects are rigid bodies in simulation. The real world has soft objects. Your robot treats grabbing an egg the same as grabbing a rock.
Temporal — no sensor drift in simulation. Run for 3 hours in the real world and the accumulated error is significant.

The simulation isn't bad. You just never figured out what information you lost before training.

The framework I keep coming back to: compress → quantify → verify → optimize.

In Sim2Real terms: compression is simulation, quantification is the benchmark score, verification is the Sim2Real gap, optimization is tuning simulation parameters. Every link in this chain needs its own independent verification method.

The industry standard practice is: skip verification, report the score.

The Silent Cost

When "good" is undefined, a significant portion of $30 billion gets misallocated. Not because the technology isn't ready — because there's no standard for measuring whether it works, so investors can only bet on storytelling.

Some teams are working on this. WorldArena Track2 tries to evaluate multi-agent collaboration closer to real-world conditions. A few international competitions added Sim2Real tracks this year.

But scattered efforts don't make a standard.

Benchmarks define direction. Whoever defines "good" defines where the industry goes.

This is the question embodied AI faces in summer 2026: the money arrived, the talent arrived, but the standard for "good" is still waiting for an answer.