<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Deep Research on Lynx Tech Blog</title><link>https://blog.lynxflow.co/en/categories/deep-research/</link><description>Recent content in Deep Research on Lynx Tech Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Sun, 13 Sep 2026 16:15:00 +0800</lastBuildDate><atom:link href="https://blog.lynxflow.co/en/categories/deep-research/index.xml" rel="self" type="application/rss+xml"/><item><title>Part 1 · Coordinate System: Why I Recommend Non-CS People Start With This Stanford CS Framework</title><link>https://blog.lynxflow.co/en/posts/self-taught-cs-coordinate-system-2026/</link><pubDate>Tue, 11 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/self-taught-cs-coordinate-system-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/self-taught-cs-coordinate-system-2026.png?v=090509" alt="Featured image of post Part 1 · Coordinate System: Why I Recommend Non-CS People Start With This Stanford CS Framework" /&gt;Part I · The Coordinate System: Why I Recommend Starting with This Stanford CS Framework for Non-CS Majors 1. Where&amp;rsquo;s Your Ceiling? Let me be blunt: people who can use AI tools are everywhere now.
I can write copy with ChatGPT, write small scripts with Claude, chain various agents into a pipeline—I do all of this, and I do it proficiently. I&amp;rsquo;ve built my own model proxy stack, run dozens of automation projects, and wield these tools more deftly than most programmers.</description></item><item><title>The Complete Technical Routes for Hermes to Control Claude Code: From Terminal Invocation to Agent Interoperability Protocols</title><link>https://blog.lynxflow.co/en/posts/hermes-claude-code-control-routes-2026/</link><pubDate>Sun, 13 Sep 2026 16:15:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/hermes-claude-code-control-routes-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/hermes-cc-control-routes-cover.png" alt="Featured image of post The Complete Technical Routes for Hermes to Control Claude Code: From Terminal Invocation to Agent Interoperability Protocols" /&gt;One-Sentence Summary Hermes (a Telegram bot gateway) controlling Claude Code (Anthropic&amp;rsquo;s CLI coding agent) has 7 technical routes: direct terminal invocation, SDK programmatic API, MCP bidirectional communication, A2A Agent-to-Agent protocol, ACP Agent Communication protocol, Hooks system, and Cloud sessions. Hermes already has codex_app_server and copilot_acp subprocess runtime templates — adding a Claude Code runtime is essentially copying an existing pattern. For Telegram bot scenarios</description></item><item><title>My Quant Platform Was Throwing Away 92% of the Trades — and Believed It Saw Everything</title><link>https://blog.lynxflow.co/en/posts/alphatrace-venue-data-loss-2026/</link><pubDate>Sun, 13 Sep 2026 12:56:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/alphatrace-venue-data-loss-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/alphatrace-venue-data-loss-2026.png" alt="Featured image of post My Quant Platform Was Throwing Away 92% of the Trades — and Believed It Saw Everything" /&gt;TL;DR While adding a data source to AlphaTrace, I ran a routine check against live data and found something I had never once doubted: the platform silently discards 92% of spot trades the moment it reads them off-chain — and says nothing. This post is about that self-correction, and about the second mistake I nearly made.
It started with someone else&amp;rsquo;s article A few days ago I read a write-up: two people ran a cross-market arbitrage between Hyperliquid&amp;rsquo;s equity perpetuals and the tra</description></item><item><title>Don't Replace the Whole Department, Just Take 10%: My Research on How AI Can Compliantly Break into Traditional Industries</title><link>https://blog.lynxflow.co/en/posts/ai-traditional-industry-10pct-research-2026/</link><pubDate>Tue, 08 Sep 2026 00:01:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/ai-traditional-industry-10pct-research-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/ai-traditional-industry-10pct-research-2026.png" alt="Featured image of post Don't Replace the Whole Department, Just Take 10%: My Research on How AI Can Compliantly Break into Traditional Industries" /&gt;I set an anchor for myself: don&amp;rsquo;t replace an entire department — replace 10% of one business line, and call 500k RMB in annual revenue a success.
That number turns &amp;ldquo;helping businesses do the math&amp;rdquo; from an empty slogan into something verifiable. It&amp;rsquo;s not big, but it&amp;rsquo;s concrete — concrete enough for me to judge whether a solution is worth building.
The stack I can put together: n8n (self-hosted workflows), multimodal model calls (vision and OCR), an API relay station</description></item><item><title>39 Trades, 10 Hypotheses — and the Top-Matching Strategy Rests on Just 3 Data Points</title><link>https://blog.lynxflow.co/en/posts/alphatrace-honest-quant-2026/</link><pubDate>Tue, 08 Sep 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/alphatrace-honest-quant-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/alphatrace-honest-quant-2026.png" alt="Featured image of post 39 Trades, 10 Hypotheses — and the Top-Matching Strategy Rests on Just 3 Data Points" /&gt;TL;DR I fed 39 on-chain trades into 10 competing strategy hypotheses on AlphaTrace, the quant analysis platform I built myself, hoping to find &amp;ldquo;what method these trades are most likely following.&amp;rdquo; The top-ranked strategy ended up resting on just 3 valid data points, and its explanatory power was the lowest of all ten hypotheses — it took the crown purely because it&amp;rsquo;s the simplest.
That&amp;rsquo;s not a bug. This is what an honest quant analysis is supposed to look like: the explan</description></item><item><title>Arbitraging $10M in 10 Months: A Deep Dive into the Hyperliquid HIP-3 Cross-Market Strategy</title><link>https://blog.lynxflow.co/en/posts/hyperliquid-hip3-arbitrage-10m-usd/</link><pubDate>Mon, 07 Sep 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/hyperliquid-hip3-arbitrage-10m-usd/</guid><description>&lt;img src="https://blog.lynxflow.co/images/hyperliquid-hip3-arbitrage-10m-usd.png" alt="Featured image of post Arbitraging $10M in 10 Months: A Deep Dive into the Hyperliquid HIP-3 Cross-Market Strategy" /&gt;TL;DR A two-person arbitrage team built a Delta arbitrage bot between Hyperliquid&amp;rsquo;s HIP-3 stock perpetual markets and the traditional brokerage IBKR. Over 10 months, it processed roughly $32 billion in trading volume and earned about $10 million in profit, with an annualized return on capital of 35%-45%.
The original piece was written by Twitter user CBB (@Cbb0fe), compiled into Chinese by Odaily (translator: Azuma), and published on September 3, 2026. The image-text notes circulating on X</description></item><item><title>From $800 to $200 a Day (Part 2): A Retail Trader's Deep Research into Crypto Quant and the LynxCrypto Development Blueprint</title><link>https://blog.lynxflow.co/en/posts/crypto-quant-lynxcrypto-blueprint/</link><pubDate>Sat, 05 Sep 2026 03:40:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/crypto-quant-lynxcrypto-blueprint/</guid><description> Part 1 recap (full Part 1 here): yesterday I launched a ZECUSDT futures grid with 200 grids across the 800–2000 USDT range. A $100 short lost, but the Martingale futures grid made $100+, ZEC spot made $20, and total assets peaked at $1,080. After withdrawing $200, I have $880 left compounding. The goal: turn 800 into 1000 every day and withdraw 200.
Part 2&amp;rsquo;s theme: I&amp;rsquo;m not going to rely on luck. This post is the deep research I did for myself — the mathematical truth about grids and</description></item><item><title>Expired Patent Intelligence: Mining Entrepreneurial Opportunities from Global Expired Patents</title><link>https://blog.lynxflow.co/en/posts/expired-patent-intelligence-startup-opportunities/</link><pubDate>Fri, 04 Sep 2026 19:30:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/expired-patent-intelligence-startup-opportunities/</guid><description>&lt;img src="https://blog.lynxflow.co/images/expired-patent-intelligence-cover.png" alt="Featured image of post Expired Patent Intelligence: Mining Entrepreneurial Opportunities from Global Expired Patents" /&gt;Every year, patents protecting billions of dollars in revenue expire—and behind these expired patents lies a massive opportunity landscape. From generic drugs to drug repurposing, from technical standards to manufacturing processes, each &amp;ldquo;patent cliff&amp;rdquo; that drops suddenly opens up previously monopolized technological space to everyone. The global generic drug market was valued at $410–490 billion in 2024, and by 2030 approximately 200 drugs will face patent expiration, representing $</description></item><item><title>What the Server Knows, the Browser Shouldn't Get: Mapping the Frontend Secret-Leakage Surface</title><link>https://blog.lynxflow.co/en/posts/frontend-secret-leakage-ai-era/</link><pubDate>Wed, 02 Sep 2026 01:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/frontend-secret-leakage-ai-era/</guid><description>&lt;img src="https://blog.lynxflow.co/images/frontend-secret-leakage-ai-era.png" alt="Featured image of post What the Server Knows, the Browser Shouldn't Get: Mapping the Frontend Secret-Leakage Surface" /&gt;Let’s start with a scenario. You’re building a SaaS product: the backend is connected to a cloud database, the frontend is Next.js, and iteration is moving fast. Before launch, you ask the security team to run a black-box test. On the third day, the report comes back with two high-severity findings:
C1: On the homepage, “View Source” reveals that the JSON inside __NEXT_DATA__ contains the full backend configuration that should have existed only on the server side—database address, message queue</description></item><item><title>Attraction Opens the Door, Trust Decides Who Stays: Dissecting a Viral Dating Maxim</title><link>https://blog.lynxflow.co/en/posts/relationship-development-attraction-trust-commitment/</link><pubDate>Tue, 01 Sep 2026 09:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/relationship-development-attraction-trust-commitment/</guid><description>&lt;img src="https://blog.lynxflow.co/images/relationship-development-attraction-trust-commitment.png" alt="Featured image of post Attraction Opens the Door, Trust Decides Who Stays: Dissecting a Viral Dating Maxim" /&gt;1. The Maxim Blends Four Different Concepts A maxim circulates in online dating discourse: &amp;ldquo;If you want a long-term relationship, display short-term traits; if you want a short-term relationship, display long-term traits.&amp;rdquo; Part of its appeal is that it captures an asymmetry people keep running into: some who only want something brief come across as devoted and eager to commit, while others who genuinely want something lasting seem bland early on and are never taken seriously. These o</description></item><item><title>14+ Executives Left OpenAI in 2026: Power Consolidation and an Organizational Slim-Down Under IPO Pressure</title><link>https://blog.lynxflow.co/en/posts/openai-2026-executive-exodus-restructuring/</link><pubDate>Sat, 29 Aug 2026 08:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/openai-2026-executive-exodus-restructuring/</guid><description>&lt;img src="https://blog.lynxflow.co/images/openai-2026-executive-exodus-restructuring.png" alt="Featured image of post 14+ Executives Left OpenAI in 2026: Power Consolidation and an Organizational Slim-Down Under IPO Pressure" /&gt;In 2026, OpenAI is going through a rare executive exodus. According to Business Insider, more than 14 executives have departed so far this year. A TechCrunch analysis from August 26, 2026 (&amp;ldquo;how do we explain OpenAI&amp;rsquo;s executive exodus&amp;rdquo;) traces the surface turbulence to a clearer throughline: the company is cutting non-revenue businesses, doubling down on commercialization, and co-founder Greg Brockman&amp;rsquo;s power is being reconsolidated.
1. What happened Scale of departures: 1</description></item><item><title>A $100K Desktop Machine That Runs Trillion-Parameter Models — NVIDIA Crammed a Supercomputer Onto Your Desk</title><link>https://blog.lynxflow.co/en/posts/msi-ws300-gb300-1t-param-workstation/</link><pubDate>Sat, 29 Aug 2026 08:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/msi-ws300-gb300-1t-param-workstation/</guid><description>&lt;img src="https://blog.lynxflow.co/images/msi-ws300-gb300-1t-param-workstation.png" alt="Featured image of post A $100K Desktop Machine That Runs Trillion-Parameter Models — NVIDIA Crammed a Supercomputer Onto Your Desk" /&gt;$99,900. Desktop form factor. Runs open-source LLMs with up to 1 trillion parameters — specs that, three years ago, would have required an entire server room.
According to an August 28 report by IT Home, the MSI XpertStation WS300 has started shipping overseas and is on sale at Newegg, powered by NVIDIA&amp;rsquo;s GB300 Grace Blackwell Ultra desktop superchip with up to 748GB of coherent memory. This is not a prototype — it&amp;rsquo;s an in-stock product sitting in regular retail channels.</description></item><item><title>A $45B Compute Stockpile: $7.5B a Year, 5x the Annualized Run of Its Last Monthly Deal</title><link>https://blog.lynxflow.co/en/posts/anthropic-nscale-45b-compute-deal/</link><pubDate>Sat, 29 Aug 2026 08:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/anthropic-nscale-45b-compute-deal/</guid><description>&lt;img src="https://blog.lynxflow.co/images/anthropic-nscale-45b-compute-deal.png" alt="Featured image of post A $45B Compute Stockpile: $7.5B a Year, 5x the Annualized Run of Its Last Monthly Deal" /&gt;A $45 billion, 6-year agreement works out to an average annual procurement intensity of about $7.5 billion — and just three months ago, Anthropic&amp;rsquo;s compute deal with SpaceX was still billed monthly, at roughly $125 million a month, or only $1.5 billion annualized. Procurement intensity jumped about 5x in a single quarter.
What&amp;rsquo;s burning isn&amp;rsquo;t compute — it&amp;rsquo;s investors&amp;rsquo; expectations for Anthropic&amp;rsquo;s revenue curve.
1. What this deal is In August, Anthropic signed</description></item><item><title>After 4 Weeks of Using AI to Check Fake News, Turning It Off Leaves You Worse Than When You Started — MIT's "Dependency Paradox"</title><link>https://blog.lynxflow.co/en/posts/mit-ai-dependency-paradox-fake-news/</link><pubDate>Sat, 29 Aug 2026 08:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/mit-ai-dependency-paradox-fake-news/</guid><description>&lt;img src="https://blog.lynxflow.co/images/mit-ai-dependency-paradox-fake-news.png" alt="Featured image of post After 4 Weeks of Using AI to Check Fake News, Turning It Off Leaves You Worse Than When You Started — MIT's "Dependency Paradox"" /&gt;In week one, people using an AI assistant to identify fake news were 21% more accurate than the control group. By week four, when the AI was taken away, their accuracy didn&amp;rsquo;t rise — it fell, ending up 15 percentage points below where they started before the experiment. Stranger still, about one in five participants &amp;ldquo;felt they had gotten better.&amp;rdquo;
This comes from a study published on August 25, 2026 by Pattie Maes&amp;rsquo;s team at the MIT Media Lab (covered the same day by MIT Tec</description></item><item><title>AI Deep Dive | The More Money Raised, the Earlier the Monetization Questions?</title><link>https://blog.lynxflow.co/en/posts/ai-funding-vs-monetization-scrutiny-2026/</link><pubDate>Sat, 29 Aug 2026 08:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/ai-funding-vs-monetization-scrutiny-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/ai-funding-vs-monetization-scrutiny-2026.png" alt="Featured image of post AI Deep Dive | The More Money Raised, the Earlier the Monetization Questions?" /&gt;Money is coming in faster than the press releases can keep up.
Around April 2026, Anthropic&amp;rsquo;s revenue run rate (annualized revenue) was widely reported at roughly $30 billion; four months later, Reuters cited a source on August 17 saying the figure had topped $65 billion. CNBC and others reported that Anthropic had overtaken OpenAI as the most valuable AI startup. Yet in that same stretch, TechCrunch set the theme running through all 200 sessions of its upcoming October Disrupt 2026 — a sl</description></item><item><title>Doubao Work: The Agent Skips Re-Onboarding and Reads Your Feishu Org Context Directly</title><link>https://blog.lynxflow.co/en/posts/doubao-work-feishu-zero-config-context/</link><pubDate>Sat, 29 Aug 2026 08:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/doubao-work-feishu-zero-config-context/</guid><description>&lt;img src="https://blog.lynxflow.co/images/doubao-work-feishu-zero-config-context.png?v=090123" alt="Featured image of post Doubao Work: The Agent Skips Re-Onboarding and Reads Your Feishu Org Context Directly" /&gt;Every mainstream office Agent can do the work — process files, generate web pages, build spreadsheets — but nearly all of them first need you to &amp;ldquo;explain the background.&amp;rdquo; ByteDance&amp;rsquo;s Doubao Work (released August 26, 2026, covered by QbitAI, TechNode, and Caixin) flips that around: log in with your Feishu enterprise account, and it reads your group chats and cloud docs directly, no re-explaining required.
What it&amp;rsquo;s going after is the &amp;ldquo;zero-config context access&amp;rdquo</description></item><item><title>Gates Hits the Brakes on AI: Tax the Robots, Reserve "Human-Only Jobs"</title><link>https://blog.lynxflow.co/en/posts/bill-gates-robot-tax-human-reserved-jobs/</link><pubDate>Sat, 29 Aug 2026 08:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/bill-gates-robot-tax-human-reserved-jobs/</guid><description>&lt;img src="https://blog.lynxflow.co/images/bill-gates-robot-tax-human-reserved-jobs.png" alt="Featured image of post Gates Hits the Brakes on AI: Tax the Robots, Reserve "Human-Only Jobs"" /&gt;On August 26, 2026, Bill Gates published a long essay on Gates Notes proposing two things that make tech giants uncomfortable: taxing robots (and AI tokens), and establishing &amp;ldquo;Human Reserved Jobs.&amp;rdquo; TechCrunch, Fortune, CBS, and Axios all ran same-day coverage.
The odd part — this tech titan is now speaking up for the labor being displaced.
1. What He Proposed Two proposals:
Robot tax: Fix a bug in the current tax code — companies pay payroll taxes when they hire people, while buying</description></item><item><title>WhatsApp Anti-Scam: AI Judges Scams On-Device, Not a Single Byte of Messages Leaves for the Cloud</title><link>https://blog.lynxflow.co/en/posts/whatsapp-scam-alert-on-device-ai-privacy/</link><pubDate>Sat, 29 Aug 2026 08:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/whatsapp-scam-alert-on-device-ai-privacy/</guid><description>&lt;img src="https://blog.lynxflow.co/images/whatsapp-scam-alert-on-device-ai-privacy.png" alt="Featured image of post WhatsApp Anti-Scam: AI Judges Scams On-Device, Not a Single Byte of Messages Leaves for the Cloud" /&gt;Anti-scam usually means scanning messages in the cloud. WhatsApp goes the opposite way—it puts the scam-detecting AI on the phone itself, so not a single byte of message content leaves the device.
In August 2026, WhatsApp began a limited test of an opt-in anti-scam feature called Scam Alert (reported the same month by Malwarebytes, PCMag, Forbes, and the Meta Engineering blog, with technical analysis from InfoQ). Its core design: classifying messages from non-contacts for scams happens entirely</description></item><item><title>Zuckerberg Wanted to Cut 60% of Some Teams with AI, Then Cancelled Round Two — Why AI Replacing Humans 'Imploded' at Meta</title><link>https://blog.lynxflow.co/en/posts/meta-project-ot-ai-replace-jobs-imploded/</link><pubDate>Sat, 29 Aug 2026 08:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/meta-project-ot-ai-replace-jobs-imploded/</guid><description>&lt;img src="https://blog.lynxflow.co/images/meta-project-ot-ai-replace-jobs-imploded.png" alt="Featured image of post Zuckerberg Wanted to Cut 60% of Some Teams with AI, Then Cancelled Round Two — Why AI Replacing Humans 'Imploded' at Meta" /&gt;In January 2026, Zuckerberg personally pushed a reorganization codenamed Project OT (Organizational Transformation): the layoff ceiling for some teams was set at 60%, with the goal of turning Meta into an &amp;ldquo;AI-native&amp;rdquo; company — tiny human teams supervising AI agents, taking over the daily work previously done by thousands of people.
Round one was executed in May. Round two was cancelled.
According to a Reuters investigative report dated August 26, 2026 (which reviewed dozens of intern</description></item><item><title>A Billion Dollars a Day, AGI Is Already a Money Printer: NVIDIA Reveals the Bottom Card of AI Commercialization</title><link>https://blog.lynxflow.co/en/posts/nvidia-agi-token-as-money-machine/</link><pubDate>Sat, 29 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/nvidia-agi-token-as-money-machine/</guid><description>&lt;img src="https://blog.lynxflow.co/images/nvidia-agi-token-as-money.png" alt="Featured image of post A Billion Dollars a Day, AGI Is Already a Money Printer: NVIDIA Reveals the Bottom Card of AI Commercialization" /&gt;AGI Is Not the Finish Line — It&amp;rsquo;s the Starting Line On the latest earnings call, Jensen Huang calmly dropped a bombshell: for many tasks, we have already achieved AGI.
He didn&amp;rsquo;t bother racing OpenAI over who crosses the line first — he flipped the table instead. Obsessing over &amp;ldquo;how exactly to define AGI&amp;rdquo; is now meaningless. The entire tech industry doesn&amp;rsquo;t even have a consensus standard for &amp;ldquo;intelligence&amp;rdquo; itself, so arguing about what the finish line loo</description></item><item><title>I Had 114 Research Agents Investigating 'Other Ways to Automate Money-Making' — Only 8 Conclusions Survived</title><link>https://blog.lynxflow.co/en/posts/2026-08-24-auto-monetization-deep-research/</link><pubDate>Mon, 24 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/2026-08-24-auto-monetization-deep-research/</guid><description> Bottom line up front: On the map of automating income, 2026 really changed two things—GitHub shuffled large bounties into an invite-only VIP tier (the public track&amp;rsquo;s critical cap dropped to $10K, with a qualification gate), and Huntr rebranded open-source bounties into AI attack-defense tournaments ($15K prize pool, which happens to be the direction my existing pipeline maps onto most cleanly). And every pretty &amp;ldquo;market size&amp;rdquo; number mostly gets wiped out after a three-panel adv</description></item><item><title>I Scraped the Pricing Pages of Wyscout, Hudl, and Metrica: Whose Playbook Should My Football AI Tool Copy?</title><link>https://blog.lynxflow.co/en/posts/2026-08-24-football-video-analysis-pricing-teardown/</link><pubDate>Mon, 24 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/2026-08-24-football-video-analysis-pricing-teardown/</guid><description> TL;DR: Prices in this market span 100× — Once Sport charges €8/mo for everything; StatsBomb runs at the £800/mo scale and you still have to talk to sales. In between sit four entirely different profit playbooks. Which one a small team should copy depends on which link of the chain you want to own.
From €8/mo to £800/mo: the video-analysis market&amp;rsquo;s price tiers | this article</description></item><item><title>Full Health Check of CPA Model Pool: Which of 80 Models Are Alive, Smart, or Running Naked</title><link>https://blog.lynxflow.co/en/posts/cpa-model-pool-audit-smart-model-ranking/</link><pubDate>Sun, 23 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/cpa-model-pool-audit-smart-model-ranking/</guid><description>&lt;img src="https://blog.lynxflow.co/images/cpa-model-pool-audit-smart-model-ranking.png" alt="Featured image of post Full Health Check of CPA Model Pool: Which of 80 Models Are Alive, Smart, or Running Naked" /&gt;Last month I wrote an article testing six Claude Code models. This time I&amp;rsquo;m broadening the scope: my local CPA (local model gateway) has 80 models connected, and I did two things — first, sent real requests to each one to check liveness, then gave the survivors a puzzle-resistant intelligence test, and finally cross-validated against public leaderboards.
Bottom line up front: listing a model ≠ it works. Of the 80 models, 59 are text models, and only about 30 could actually hold a conversat</description></item><item><title>Installing a Control Plane for 'Script + Cron + Agent' Black-Box Automation: A Survey of 12 Platforms</title><link>https://blog.lynxflow.co/en/posts/2026-08-23-workflow-control-plane-selection/</link><pubDate>Sun, 23 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/2026-08-23-workflow-control-plane-selection/</guid><description>&lt;img src="https://blog.lynxflow.co/images/2026-08-23-control-plane-architecture-cover.png" alt="Featured image of post Installing a Control Plane for 'Script + Cron + Agent' Black-Box Automation: A Survey of 12 Platforms" /&gt; TL;DR: What I need isn&amp;rsquo;t another orchestration tool, but a Control Plane—a coordination layer that answers in one place: which workflow, which version, which run, which step failed, how many retries, when&amp;rsquo;s the next run. cron only knows how to &amp;ldquo;knock on time&amp;rdquo; and then walks away. After surveying 11 platforms, my pick: Windmill first choice, Kestra co-selected; n8n/Temporal/Argo explicitly rejected; Hermes demoted to &amp;ldquo;scheduled agent&amp;rdquo; instead of scheduler.</description></item><item><title>Football Scouting Data Landscape: A Breakdown of Five Key Competitors and a Solo Founder's Niche</title><link>https://blog.lynxflow.co/en/posts/2026-08-22-football-scouting-competitor-landscape/</link><pubDate>Sat, 22 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/2026-08-22-football-scouting-competitor-landscape/</guid><description>&lt;img src="https://blog.lynxflow.co/images/2026-08-22-football-scouting-competitor-landscape.png" alt="Featured image of post Football Scouting Data Landscape: A Breakdown of Five Key Competitors and a Solo Founder's Niche" /&gt; TL;DR: This piece started as a happy accident. I set out to check whether a small chemical company called &amp;ldquo;Yang Li&amp;rdquo; on Qian Nong 1st Road in Xiaoshan was undercutting business, and discovered that the moat in chemical manufacturing — hazardous-material licenses, process know-how, and upstream supply lock-ups — is completely impenetrable to outsiders. But the idea that emerged from that research, a &amp;ldquo;supply-chain data product,&amp;rdquo; translates perfectly to football scouting dat</description></item><item><title>How Much Compensation and Benefits Do Families Receive After a Police Officer's Line-of-Duty Death? (A Twelve-Country Comparison)</title><link>https://blog.lynxflow.co/en/posts/2026-08-22-police-death-benefits-12-countries/</link><pubDate>Sat, 22 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/2026-08-22-police-death-benefits-12-countries/</guid><description>&lt;img src="https://blog.lynxflow.co/images/police-death-benefits-12-countries.png" alt="Featured image of post How Much Compensation and Benefits Do Families Receive After a Police Officer's Line-of-Duty Death? (A Twelve-Country Comparison)" /&gt;When a Police Officer Falls in the Line of Duty, What Does the State Leave to Their Family? The answer varies far more between countries than most people imagine: some issue a cheque worth millions of yuan; others pay an annuity monthly until the surviving spouse dies; some cover tuition all the way through university; and in yet others, you can&amp;rsquo;t even produce a single reliable figure — the absence of published data is, in itself, an answer.</description></item><item><title>Image-by-Image Breakdown: The 14 Images in a Classic tmux Tutorial and How Each One Works</title><link>https://blog.lynxflow.co/en/posts/tmux-delicious-brains-teardown/</link><pubDate>Sat, 22 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/tmux-delicious-brains-teardown/</guid><description>&lt;img src="https://blog.lynxflow.co/images/tmux-teardown-cover.png" alt="Featured image of post Image-by-Image Breakdown: The 14 Images in a Classic tmux Tutorial and How Each One Works" /&gt; Original: tmux for Local Development: A Deep Dive by Jeff Cole, published on Delicious Brains in May 2016. Note: The original site was later redesigned (images replaced with static PNG remakes), and the 2016 original GIFs are no longer available online. Therefore, the images in this post are reproduced by re-running each command and configuration from the original article step-by-step in tmux 3.4 on a local machine. The visual content corresponds exactly to the original (with individual differe</description></item><item><title>RSS to Social Media Auto Distribution: A Panoramic Survey of Open-Source Projects (The Peers of LynxPipe)</title><link>https://blog.lynxflow.co/en/posts/rss-to-social-media-oss-pipeline-landscape/</link><pubDate>Sat, 22 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/rss-to-social-media-oss-pipeline-landscape/</guid><description>&lt;img src="https://blog.lynxflow.co/images/2026-08-22-rss-social-oss-four-layer-cover.png" alt="Featured image of post RSS to Social Media Auto Distribution: A Panoramic Survey of Open-Source Projects (The Peers of LynxPipe)" /&gt; TL;DR: There&amp;rsquo;s no single open-source project that handles &amp;ldquo;RSS aggregation → processing → social distribution&amp;rdquo; end-to-end, but every layer has mature tools. The combination closest to LynxPipe&amp;rsquo;s full form is RSSHub (sources) + Huginn/n8n (processing) + Postiz (distribution), with a combined star total exceeding 280k. Here&amp;rsquo;s the full research: each project&amp;rsquo;s star count (measured via GitHub API on 2026-08-22), RSS capabilities, and supported social channels.</description></item><item><title>I Killed the New Outlook's Ads with One Line in hosts</title><link>https://blog.lynxflow.co/en/posts/block-outlook-ads-hosts/</link><pubDate>Fri, 21 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/block-outlook-ads-hosts/</guid><description>&lt;img src="https://blog.lynxflow.co/images/block-outlook-ads-hosts.png?v=0821" alt="Featured image of post I Killed the New Outlook's Ads with One Line in hosts" /&gt;Every time I opened the new Outlook, an Adobe ad sat on top of the inbox—Photoshop, Acrobat, rotating, all in Japanese. My first instinct, like most people: switch back to classic Outlook, the one without ads.
A quick probe of the machine showed that step is a dead end.
Switch to classic? It&amp;rsquo;s not even installed I ran a PowerShell snippet from WSL to map the machine. The verdict was blunt:</description></item><item><title>One Pill Lasts All Day? How Active Metabolites Extend Drug Effects</title><link>https://blog.lynxflow.co/en/posts/active-metabolites-once-daily-dosing/</link><pubDate>Fri, 21 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/active-metabolites-once-daily-dosing/</guid><description>&lt;img src="https://blog.lynxflow.co/images/active-metabolites-once-daily-dosing.png" alt="Featured image of post One Pill Lasts All Day? How Active Metabolites Extend Drug Effects" /&gt;An Anti-Intuitive Phenomenon Many psychiatric medications are taken only once daily, yet the drugs themselves often have relatively short half-lives. Take bupropion—a norepinephrine-dopamine reuptake inhibitor (NDRI) used for depression, smoking cessation, and ADHD. Its immediate-release formulation has a half-life of approximately 21 hours. By classic pharmacokinetic principles, it should be dosed three times daily (TID) to maintain steady-state concentrations. Yet clinically, bupropion XL (ext</description></item><item><title>A Comparative Review of Eight IDEs in the AI Era: Memory Usage, Runtime Efficiency, and Bug Signals (Multi-Source Verification, August 2026)</title><link>https://blog.lynxflow.co/en/posts/2026-08-19-ide-comparison-memory-efficiency-bugs/</link><pubDate>Wed, 19 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/2026-08-19-ide-comparison-memory-efficiency-bugs/</guid><description>&lt;img src="https://blog.lynxflow.co/images/2026-08-19-ide-memory-footprint.png" alt="Featured image of post A Comparative Review of Eight IDEs in the AI Era: Memory Usage, Runtime Efficiency, and Bug Signals (Multi-Source Verification, August 2026)" /&gt;Stop taking that optimistic little “lightweight 400MB” number on IDE landing pages at face value—the 2026 generation of AI IDEs has entered the era of “memory inflation.” In a Copilot Chat session stretched across 18 hours, the VS Code extension host can balloon from 500MB to 80GB+, while Zed has even recorded an extreme 185GB case on macOS.
This article offers an in-depth comparison of eight mainstream AI IDEs. Using multi-source online verification plus adversarial review, we cross-check the a</description></item><item><title>ccswitch Edited the Model, Claude Code Didn't Budge: A Three-Layer Override Root-Cause Diagnosis</title><link>https://blog.lynxflow.co/en/posts/ccswitch-model-not-applying-claude-code/</link><pubDate>Tue, 18 Aug 2026 21:36:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/ccswitch-model-not-applying-claude-code/</guid><description>&lt;img src="https://blog.lynxflow.co/images/ccswitch-model-not-applying-claude-code.png?v=090603" alt="Featured image of post ccswitch Edited the Model, Claude Code Didn't Budge: A Three-Layer Override Root-Cause Diagnosis" /&gt;Origin: Three Edits, Zero Effect I opened ccswitch (v3.19.2, the Windows-side Claude Code config switcher), clicked &amp;ldquo;edit provider,&amp;rdquo; changed the Opus-tier model, and saved. Restarted Claude Code — still running the old model. Edited, saved, restarted again — still no change.
&amp;ldquo;Doesn&amp;rsquo;t change&amp;rdquo; problems are the worst kind to guess at. Below is the process of forcing out the root cause with logs and a field-by-field database comparison. The conclusion is more tangled th</description></item><item><title>The Free 'Unlimited' LLM API — I Curled Every One</title><link>https://blog.lynxflow.co/en/posts/free-unlimited-llm-api-verified-2026/</link><pubDate>Tue, 18 Aug 2026 01:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/free-unlimited-llm-api-verified-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/free-unlimited-llm-api-verified-2026.png?v=0818b" alt="Featured image of post The Free 'Unlimited' LLM API — I Curled Every One" /&gt;It started with a message: Agnes AI&amp;rsquo;s Agnes-2.0-Flash text model API has been free and unlimited since June 1, 2026 — 1M context, OpenAI-compatible. My first reaction: nice. My second: in the AI world, &amp;ldquo;unlimited&amp;rdquo; basically means &amp;ldquo;I bet you won&amp;rsquo;t actually max it out.&amp;rdquo; So I curled every one.
Round 1: The deep-research workflow face-planted I fired up a full Grok deep-research workflow (5 parallel agents + adversarial verification + synthesis) to check Agnes and</description></item><item><title>Singapore 4c8t + 64GB dedicated server: what’s the lowest price you can get? I went through OVH’s product line</title><link>https://blog.lynxflow.co/en/posts/ovh-sg-server-selection/</link><pubDate>Tue, 18 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/ovh-sg-server-selection/</guid><description>&lt;img src="https://blog.lynxflow.co/images/ovh-kimsufi-pricing.png?v=082421" alt="Featured image of post Singapore 4c8t + 64GB dedicated server: what’s the lowest price you can get? I went through OVH’s product line" /&gt;It started with a monitoring screenshot someone shared: 8 threads, 64GB RAM, 4TB disk, Singapore location, unmetered traffic. I had a bunch of always-on services—more than a dozen containers and gateways besides Claude Code—that I was looking to move somewhere, so I wanted to know: what’s the lowest price for this kind of setup? I ended up going through several OVH product lines. This article is the condensed version, with the numbers and the pitfalls below.</description></item><item><title>ChatGPT pro 20x Subscription vs China's Top LLMs: Per-Million-Token Cost</title><link>https://blog.lynxflow.co/en/posts/pro20x-vs-china-llm/</link><pubDate>Mon, 17 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/pro20x-vs-china-llm/</guid><description>&lt;img src="https://blog.lynxflow.co/images/pro20x-vs-china-llm.png?v=090818" alt="Featured image of post ChatGPT pro 20x Subscription vs China's Top LLMs: Per-Million-Token Cost" /&gt;A counterintuitive ledger Suppose you pay 1000 RMB to activate a ChatGPT &amp;ldquo;pro 20x&amp;rdquo; subscription, equivalent to roughly $1700 of API value per week, and stack OpenAI&amp;rsquo;s ~10 monthly &amp;ldquo;Goodwill Resets&amp;rdquo; (announced by tibo — each one refreshes a brand-new fully-loaded 7-day cycle). Under that assumption, your effective cost per million tokens lands at 0.036 ~ 0.91 RMB — while China&amp;rsquo;s cheapest DeepSeek-V4-Flash costs 1.06 RMB, ~1.2x higher; against the priciest Kimi K</description></item><item><title>I Dug Through the Source Code of 28 Open-Source AI Comic-Drama Platforms: Every Claim of a “Pluggable Provider” Fell Apart</title><link>https://blog.lynxflow.co/en/posts/open-source-ai-drama-platform-selection-audit-2026/</link><pubDate>Sun, 16 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/open-source-ai-drama-platform-selection-audit-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/open-source-ai-drama-platform-selection-audit-2026-cover.jpg" alt="Featured image of post I Dug Through the Source Code of 28 Open-Source AI Comic-Drama Platforms: Every Claim of a “Pluggable Provider” Fell Apart" /&gt;Why This Started: I Want to Build AI Comics, but I Don’t Want to Build the Platform from Scratch I already have a working model backend: an OpenAI-compatible image/video gateway, plus Alibaba Cloud Bailian (Wanxiang for images and wan2.x for video). What I’m missing is the upper layer—a director’s workbench that can manage the full flow from “script → characters → storyboards → image generation → video generation → compositing.”</description></item><item><title>I Dug Through the Source Code of 57 Open-Source AI Novel-Writing Projects: The One with the Highest Engineering Score Lost on Its License</title><link>https://blog.lynxflow.co/en/posts/open-source-ai-novel-writing-platform-selection-audit-2026/</link><pubDate>Sun, 16 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/open-source-ai-novel-writing-platform-selection-audit-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/ai-novel-writing-audit-2026-cover.png" alt="Featured image of post I Dug Through the Source Code of 57 Open-Source AI Novel-Writing Projects: The One with the Highest Engineering Score Lost on Its License" /&gt;The Starting Point: If AI Is Writing Long-Form Fiction, Where Should the Platform Come From? I’m building an AI long-form fiction production system. The backend isn’t the problem: I already have a self-hosted OpenAI-compatible gateway, plus Alibaba Cloud Bailian’s Qwen family, all running through a pure-text pipeline. What’s missing is the upper layer—a writing workbench that can manage “outline → chapter planning → chapter generation → memory → revision → export.”</description></item><item><title>Hands-on Test of Six Claude Code Models: Who’s Fast, Who’s Smart, and Who’s a Lottery</title><link>https://blog.lynxflow.co/en/posts/claude-code-model-benchmark/</link><pubDate>Sat, 15 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/claude-code-model-benchmark/</guid><description>&lt;img src="https://blog.lynxflow.co/images/cc-bench-hero.png?v=090603" alt="Featured image of post Hands-on Test of Six Claude Code Models: Who’s Fast, Who’s Smart, and Who’s a Lottery" /&gt;Claude Code’s model picker currently has six options sitting in it: qwen3.8-max, glm-5.2-fast-preview, glm-5.2, deepseek-v4-pro-0813, qwen3-coder-next, and grok-4.6. All of them are routed through my local CPA, a local model gateway, to their respective upstream providers. In day-to-day use, my impressions were vague: “this one feels faster,” “that one feels smarter.” Gut feel is unreliable, so I spent an evening putting them on the same starting line and benchmarked speed, thinking time, long-i</description></item><item><title>Choosing a Primary Model for Your Everyday Coding Agent: Stop Looking Only at Who Ranks First on SWE-bench</title><link>https://blog.lynxflow.co/en/posts/2026-08-13-coding-agent-benchmark-selection/</link><pubDate>Thu, 13 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/2026-08-13-coding-agent-benchmark-selection/</guid><description>&lt;img src="https://blog.lynxflow.co/images/2026-08-13-coding-agent-benchmark-selection.png" alt="Featured image of post Choosing a Primary Model for Your Everyday Coding Agent: Stop Looking Only at Who Ranks First on SWE-bench" /&gt; Bottom line first: if you use Claude Code / Codex for everyday coding, you should no longer treat “#1 on SWE-bench” as the only yardstick for choosing your main model. In 2026, the more reliable public stack is Terminal-Bench + SWE-rebench/DeepSWE + a cost dashboard; the final verdict still has to come from tasks in your own repositories.
What you’re evaluating is not “can it write a function,” but “can it act like an engineer over time” If your workflow looks like this:</description></item><item><title>Coding Agent Candidate Scorecard: Grok 4.6 / Qwen3.8-Max / DeepSeek-V4 Pro / GLM-5.2</title><link>https://blog.lynxflow.co/en/posts/2026-08-13-coding-agent-candidate-models-scorecard/</link><pubDate>Thu, 13 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/2026-08-13-coding-agent-candidate-models-scorecard/</guid><description>&lt;img src="https://blog.lynxflow.co/images/2026-08-13-coding-agent-candidate-models-scorecard.png" alt="Featured image of post Coding Agent Candidate Scorecard: Grok 4.6 / Qwen3.8-Max / DeepSeek-V4 Pro / GLM-5.2" /&gt; TL;DR: This is the targeted follow-up to “How to pick your daily Coding Agent model — stop worshipping SWE-bench #1” — we put four candidates (grok-4.6, qwen3.8-max, deepseek-v4-pro, glm-5.2) on official leaderboards and pinned down each score. None of the four beats the public frontier (claude-opus-5 74% / gpt-5.6-sol 73% / fable-5 70%); but on “cheap + good enough” there is a clear route: GLM-5.2 for 80% daily work, Grok 4.6 for hard tasks, DeepSeek-V4 Pro as the cheap sub-agent. The key red</description></item><item><title>Furniture Virtual Try-On: The Competitor Landscape, Capability Matrix, and the China Gap</title><link>https://blog.lynxflow.co/en/posts/2026-08-13-furniture-tryon-competitor-landscape/</link><pubDate>Thu, 13 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/2026-08-13-furniture-tryon-competitor-landscape/</guid><description>&lt;img src="https://blog.lynxflow.co/images/2026-08-13-furniture-tryon-competitor-landscape.png" alt="Featured image of post Furniture Virtual Try-On: The Competitor Landscape, Capability Matrix, and the China Gap" /&gt; This is the competitor companion to the Furniture Virtual Try-On: H5-first + 2D composition + image-to-video plan. That piece covers how to build; this one covers who the players are, where they&amp;rsquo;re strong, and where they die. Data comes from Grok deep research + an 8-source competitor scan (57 entries, hallucinated entries like the phantom &amp;ldquo;MeiKeEr&amp;rdquo; stripped out), then adversarially verified by a 105-agent workflow (25 claims → 4 survived + 21 killed). Prices are point-in-time</description></item><item><title>Goodbye SaaS: I Cloned 7 Open-Source Social Media Distributors and Read Every One's Code</title><link>https://blog.lynxflow.co/en/posts/open-source-social-distributor-survey/</link><pubDate>Thu, 13 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/open-source-social-distributor-survey/</guid><description>&lt;img src="https://blog.lynxflow.co/images/open-source-social-distributor-survey.png" alt="Featured image of post Goodbye SaaS: I Cloned 7 Open-Source Social Media Distributors and Read Every One's Code" /&gt;I&amp;rsquo;ve used the SaaS multi-posters (the Xiaodouyai type) — one click syncs a post to a dozen platforms. Convenient, but your account cookies live in their cloud, rate limits are billed monthly, and when a platform changes its UI you wait for them to patch it. This year I want to move to open-source self-hosting — keep my accounts in my own hands, be able to edit the code, stop paying subscriptions. Possibly even fork one into my own distribution base.</description></item><item><title>My Auto-Update Kept Running Late, So I Counted Upstream's Release Hours</title><link>https://blog.lynxflow.co/en/posts/cpa-autoupdate-release-schedule/</link><pubDate>Thu, 13 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/cpa-autoupdate-release-schedule/</guid><description>&lt;img src="https://blog.lynxflow.co/images/cpa-autoupdate-release-schedule.png" alt="Featured image of post My Auto-Update Kept Running Late, So I Counted Upstream's Release Hours" /&gt;The auto-update ran fine. It just ran at the wrong time. Last month I set up auto-update for my self-hosted CPA (CLIProxyAPI, a Claude proxy service): every day at 4 AM, a script pulls the upstream code, compiles, and restarts the service. I thought the time was clever — the middle of the night, nobody&amp;rsquo;s using it, plenty of room for a restart window.
This morning the update ran as usual and bumped me to v7.2.130. Then at around 2 PM the upstream shipped v7.2.131.</description></item><item><title>Managing AI agents in the terminal: I ended up keeping herdr — a head-to-head review of four managers</title><link>https://blog.lynxflow.co/en/posts/terminal-agent-manager-comparison-2026/</link><pubDate>Tue, 11 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/terminal-agent-manager-comparison-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/terminal-agent-manager-comparison-2026.png" alt="Featured image of post Managing AI agents in the terminal: I ended up keeping herdr — a head-to-head review of four managers" /&gt;Prologue: First, Getting Tangled Up by My Own Pile of AI Agents My recent workflow has turned into this: several AI agents running in the terminal at the same time—Claude Code modifying one project, Codex doing research in another, and yet another one generating content. Multiple projects in parallel, with the terminal as the main arena.
Then came the problem: I had no idea which agent was stuck, what it was waiting for, or whether it had finished. I tried tmux, used it for a while, then dropped</description></item><item><title>Video Deduplication and “Pseudo-Original” Content: A Real-World Test of Platforms’ Six-Layer Duplicate-Detection Mechanisms and Tactics</title><link>https://blog.lynxflow.co/en/posts/video-dedup-pseudo-original-2026/</link><pubDate>Mon, 10 Aug 2026 03:46:08 +0000</pubDate><guid>https://blog.lynxflow.co/en/posts/video-dedup-pseudo-original-2026/</guid><description>Someone asked what the video deduplication and “pseudo-original” content space looks like right now. I went through the whole chain from the beginning—how platforms detect duplicates, what techniques commercial deduplication services use, whether open-source repos can reproduce them, and whether this can be turned into a product. Below is what I found, organized by layer and experimental data. I’ll leave the trade-offs to readers to judge for themselves.
Platform duplicate detection is not one l</description></item><item><title>Did Baxian! Really Make 10×? AI Traces, Real Costs, and the Box-Office Math Behind a Hit</title><link>https://blog.lynxflow.co/en/posts/baxian-ai-traces-and-real-box-office-return-2026/</link><pubDate>Mon, 10 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/baxian-ai-traces-and-real-box-office-return-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/baxian-ai-traces-and-real-box-office-return-2026.png" alt="Featured image of post Did Baxian! Really Make 10×? AI Traces, Real Costs, and the Box-Office Math Behind a Hit" /&gt;Intro: A Hit Whose Formula Got Misread In the 2026 summer corridor, Baxian! became a phenomenon for Chinese animation: 23 days in, it had crossed 1.3–1.4 billion yuan, with Maoyan/Beacon predicting a final landing around 2.1 billion, and a Douban opening score of 8.3. The line that spread widest was &amp;ldquo;100 million cost, a billion-plus box office — a tenfold return.&amp;rdquo; For anyone grinding in the short-form animation (manju) space who occasionally wonders whether to leap into film, that nu</description></item><item><title>Dissecting 371 Zhihu Answers: How Public Opinion Around "350,000 Yuan to Investigate Mo Yan's Family Background" Was Ignited</title><link>https://blog.lynxflow.co/en/posts/moyan-zhihu-371-answers/</link><pubDate>Sun, 09 Aug 2026 21:30:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/moyan-zhihu-371-answers/</guid><description>&lt;img src="https://blog.lynxflow.co/images/moyan-zhihu-371-answers.png?v=090500" alt="Featured image of post Dissecting 371 Zhihu Answers: How Public Opinion Around "350,000 Yuan to Investigate Mo Yan's Family Background" Was Ignited" /&gt;This Is the Second Half of the Previous Piece A couple of days ago I wrote “350,000 Yuan Over Six Years: How Should We Judge the Cost of Studying Mo Yan?”. That one was an opinion piece. After publishing it, I felt uneasy: opinions are one thing, but were the facts complete? What exactly were all those people arguing about in the comments?
So over the past two days I did some grunt work: I dug through the project’s background in detail, then scraped 371 answers under that Zhihu question out of a</description></item><item><title>After Reading the News About Robot Dog Security Guards, I Almost Started a Robot Dog Company—After a Day of Research, I Archived the Idea</title><link>https://blog.lynxflow.co/en/posts/robot-dog-security-company-reality-check/</link><pubDate>Sun, 09 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/robot-dog-security-company-reality-check/</guid><description>&lt;img src="https://blog.lynxflow.co/images/robot-dog-security-company-reality-check.png" alt="Featured image of post After Reading the News About Robot Dog Security Guards, I Almost Started a Robot Dog Company—After a Day of Research, I Archived the Idea" /&gt;One News Story That Almost Made Me Start a Project on Impulse A few days ago, I came across a news story: Business Insider reported that robot dogs in the United States are entering the security industry—patrolling data centers, guarding high-value crops, and monitoring stadiums. The most eye-catching figure was this: using a robot dog to cover a 24/7 security post can save $80,000 to $130,000 per year compared with hiring a human guard.</description></item><item><title>AI Solves a 25-Year-Old Open Mathematical Problem in Communications</title><link>https://blog.lynxflow.co/en/posts/ai-cracks-25-year-mimo-detection-problem/</link><pubDate>Sun, 09 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/ai-cracks-25-year-mimo-detection-problem/</guid><description>&lt;img src="https://blog.lynxflow.co/images/ai-cracks-25-year-mimo-detection-problem.png" alt="Featured image of post AI Solves a 25-Year-Old Open Mathematical Problem in Communications" /&gt;A Problem That Had Been Stuck for 25 Years Was Solved by AI in a Week There is a classic hard problem in wireless communications called MIMO detection: the transmitter packs N bits into an N×N channel and sends them out; the signal gets scrambled and mixed with noise along the way, and the receiver has to recover the original bits exactly.
In theory, there is a brute-force approach: enumerate all 2^N possible bit combinations and find the one that best matches. But once N gets even moderately la</description></item><item><title>Turn Off the Vendor’s Scratchpad and Hand the Model a New One: A Simple Attack for Extracting AI Reasoning</title><link>https://blog.lynxflow.co/en/posts/extracting-llm-reasoning-traces-via-tool-params/</link><pubDate>Sun, 09 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/extracting-llm-reasoning-traces-via-tool-params/</guid><description>&lt;img src="https://blog.lynxflow.co/images/extracting-llm-reasoning-traces-via-tool-params.png?v=090500" alt="Featured image of post Turn Off the Vendor’s Scratchpad and Hand the Model a New One: A Simple Attack for Extracting AI Reasoning" /&gt;Vendors Locked the Front Door, but Forgot the Window Major model vendors have reached an unspoken consensus: you don’t get to see the raw reasoning process. OpenAI, Anthropic, and Google only return either a “summarized thought process” or a chunk of encrypted data through their APIs, which you can pass back unchanged so the model can continue the context, but you cannot read its contents.
The rationale is perfectly legitimate: raw reasoning may contain API keys, email addresses, access tokens,</description></item><item><title>AI Compute → Data Center → Power &amp; Energy: A Five-Year Buy-Side Screening Report</title><link>https://blog.lynxflow.co/en/posts/ai-compute-datacenter-energy-buy-side-research-2026/</link><pubDate>Sat, 08 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/ai-compute-datacenter-energy-buy-side-research-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/ai-compute-datacenter-energy-buy-side-research-2026.png" alt="Featured image of post AI Compute → Data Center → Power &amp; Energy: A Five-Year Buy-Side Screening Report" /&gt;Report Note This is a research framework and stock screening report, not investment advice. Data basis: macro and industry data from IEA, National Energy Administration, TrendForce, SemiAnalysis, Bloomberg, and company IR (mid-2026); financials from 2026 Q1 earnings / 2025 annual reports; valuation snapshot as of 2026-06-26 close. All conclusions distinguish &amp;ldquo;certainty / growth / valuation attractiveness / risk&amp;rdquo;.
Report Notes This report is a research framework and target-screening r</description></item><item><title>Commercial Research Report on Stacked Trash Bags (Load 10 at Once)</title><link>https://blog.lynxflow.co/en/posts/nested-trash-bag-market-research/</link><pubDate>Sat, 08 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/nested-trash-bag-market-research/</guid><description>&lt;img src="https://blog.lynxflow.co/images/nested-trash-bag-market-research.png" alt="Featured image of post Commercial Research Report on Stacked Trash Bags (Load 10 at Once)" /&gt; Research date: 2026-08-03 Research method: Tavily + Grok dual-engine online cross-verification (Chinese/English/patent databases) Status: One-off closing research; pending user decision on whether to greenlight
1. Idea Description User pain point: The current process of lining a trash can is: tear one bag off a roll, unfold it, turn it over, and fit it onto the trash can; then tear off another one, unfold it, and fit it inside the previous bag. Repeat 5-10 times so that one setup lasts for 5-10</description></item><item><title>Contest and Channel Research for Free GPT/Claude Credits</title><link>https://blog.lynxflow.co/en/posts/free-gpt-claude-credits-competitions/</link><pubDate>Sat, 08 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/free-gpt-claude-credits-competitions/</guid><description>&lt;img src="https://blog.lynxflow.co/images/free-gpt-claude-credits-competitions.png" alt="Featured image of post Contest and Channel Research for Free GPT/Claude Credits" /&gt; Generated: 2026-08-02 · Method: ultracode workflow · 30-agent (9-way Tavily + Grok dual-engine search → 20 candidates verified one by one → three-dimensional ranking synthesis) Data: 138 search hits, 133 deduplicated candidates, 20 verified, 16 usable, 4 rejected Audience: Hangzhou solo-indie developers · Goal: Maximize free GPT/Claude credits · Secondary: Win some prizes while at it
The Hangzhou Solo Dev&amp;rsquo;s Guide to Free GPT &amp;amp; Claude Credits TL;DR: Focus on these three—here&amp;rsquo;s wh</description></item><item><title>Next-Gen Wearable Active Cooling: Deep Research Beyond Traditional PCM</title><link>https://blog.lynxflow.co/en/posts/next-gen-wearable-active-cooling-research/</link><pubDate>Sat, 08 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/next-gen-wearable-active-cooling-research/</guid><description>&lt;img src="https://blog.lynxflow.co/images/next-gen-wearable-active-cooling-research.png" alt="Featured image of post Next-Gen Wearable Active Cooling: Deep Research Beyond Traditional PCM" /&gt; Research Methodology: Parallel research across 8 domains (PCM / passive / active / hybrid / intelligent control / cross-domain / competitors / manufacturing patents) → 49 raw concepts → deduplicated to 46 → adversarial thermodynamic + TRL validation for each concept → 27 survivors → Top 20 synthesis → scoring and ranking → dual-perspective red-team review (thermodynamics hardliners + commercial OEM realists). Hard Constraints: Prototypable within 12 months using commercially available component</description></item><item><title>Telegram &amp; Discord: Desktop vs Web Deep Comparison (2026)</title><link>https://blog.lynxflow.co/en/posts/tg-discord-desktop-vs-web-2026/</link><pubDate>Sat, 08 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/tg-discord-desktop-vs-web-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/tg-discord-desktop-vs-web-2026.png" alt="Featured image of post Telegram &amp; Discord: Desktop vs Web Deep Comparison (2026)" /&gt;One-sentence takeaway: For users under memory pressure (especially 8GB RAM on Windows 11 + WSL2), Discord should be moved to the web version or a lightweight client (saving 500MB–1.5GB), Telegram is better switched to the WebK version or Unigram (saving 100–300MB), while WeChat should remain on the desktop app for better stability; after moving all three apps to web versions, total memory usage drops from 3.5–5GB to 1.5–2.5GB, but you need to accept the risks of delayed notifications and unstabl</description></item><item><title>Virtual Furniture Placement: H5 First + 2D Compositing + Image-to-Video Output Workflow</title><link>https://blog.lynxflow.co/en/posts/furniture-virtual-tryon-h5-plan/</link><pubDate>Sat, 08 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/furniture-virtual-tryon-h5-plan/</guid><description>&lt;img src="https://blog.lynxflow.co/images/furniture-virtual-tryon-h5-plan.png?v=090509" alt="Featured image of post Virtual Furniture Placement: H5 First + 2D Compositing + Image-to-Video Output Workflow" /&gt; Date: 2026-08-05 · Status: Proposal pending final decision
Data sources: Grok deep research (under adversarial verification; pricing details pending) + dedicated scan of 8 competitor categories (57 entries, hallucinated items cleaned) + technical report from sibling conversation (unverified; overly optimistic figures corrected)
The competitor matrix is for directional reference only. Actual pricing/features should be checked against official websites.
1. Restating the Need Build a small-busines</description></item><item><title>Where Do LLM Companies Get Their Data? And How to Sell Data to Them</title><link>https://blog.lynxflow.co/en/posts/llm-data-procurement-sell-data-to-ai/</link><pubDate>Sat, 08 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/llm-data-procurement-sell-data-to-ai/</guid><description>&lt;img src="https://blog.lynxflow.co/images/llm-data-procurement-sell-data-to-ai.png" alt="Featured image of post Where Do LLM Companies Get Their Data? And How to Sell Data to Them" /&gt; Research date: 2026-08-05 · Research method: two parallel ultracode workflows (Grok deep research with 106 agents + local sources across 6 angles with 10 agents), totaling 116 sub-agents and 4,500+ tool calls; key conclusions were validated through 3-vote adversarial review (25 claims submitted, only 3 survived with unanimous approval), while the rest are labeled by strength of evidence. One-sentence usage guide: Use “High confidence” for decision-making; treat “Medium/Low confidence” as leads;</description></item><item><title>AI Token-Intensive Annotation Industry Whitepaper (2026)</title><link>https://blog.lynxflow.co/en/posts/ai-token-annotation-whitepaper-2026/</link><pubDate>Wed, 05 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/ai-token-annotation-whitepaper-2026/</guid><description>&lt;img src="https://blog.lynxflow.co/images/ai-token-annotation-whitepaper-2026.png" alt="Featured image of post AI Token-Intensive Annotation Industry Whitepaper (2026)" /&gt; Generated: 2026-07-31 · ultracode multi-agent research pipeline (41 agents) · Nine major sections + in-depth analysis of 12 sub-sectors Data sources are provided in the “References” section of each chapter; all judgments without sources are marked [Inferred]</description></item><item><title>Paid to Chill Without Leaving Home: A Deep Dive into Remote Online Gigs</title><link>https://blog.lynxflow.co/en/posts/paid-to-chill-without-leaving-home-a-deep-dive-into-remote-online-gigs/</link><pubDate>Wed, 05 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/paid-to-chill-without-leaving-home-a-deep-dive-into-remote-online-gigs/</guid><description>&lt;img src="https://blog.lynxflow.co/images/paid-to-chill-without-leaving-home-a-deep-dive-into-remote-online-gigs.png" alt="Featured image of post Paid to Chill Without Leaving Home: A Deep Dive into Remote Online Gigs" /&gt;In the previous post, &amp;ldquo;Which Jobs Are Vibe-Coding Friendly?&amp;rdquo;, I covered 102 jobs across 12 categories — but almost all of them require leaving home: guard booths, convenience stores, data centers. This post covers a more radical option: remote online gigs where you never leave the house at all.
The conclusion up front: the remote track works as a supplement and a bridge, not as a main line. Three reasons: low income ceiling (mostly 3,000-6,000 CNY/month), instability (task-based, the</description></item><item><title>Which Jobs Are Vibe-Coding Friendly? A Deep Dive into 100+ 'Paid-to-Chill' Jobs</title><link>https://blog.lynxflow.co/en/posts/which-jobs-are-vibe-coding-friendly-a-deep-dive-into-100-paid-to-chill-jobs/</link><pubDate>Wed, 05 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/which-jobs-are-vibe-coding-friendly-a-deep-dive-into-100-paid-to-chill-jobs/</guid><description>&lt;img src="https://blog.lynxflow.co/images/which-jobs-are-vibe-coding-friendly-a-deep-dive-into-100-paid-to-chill-jobs.png" alt="Featured image of post Which Jobs Are Vibe-Coding Friendly? A Deep Dive into 100+ 'Paid-to-Chill' Jobs" /&gt;A while ago, with cash flow tight, I researched a serious question: is there a job that pays a monthly salary like a proper job, but leaves most of your time free to openly work on your own code?
The answer is yes — and there are far more of them than I expected. I call them &amp;ldquo;paid-to-chill jobs.&amp;rdquo; I spent a week combing through Hangzhou (focused on Qiantang/Xiasha/Xiaoshan/Binjiang) and built a quick-reference table of 102 jobs across 12 categories. This post is the distilled essence.</description></item><item><title>That Childhood Sports Car Dream—Maybe You Really Can Build One Yourself: The Truth About the Roadster “Open Source” Release and a Complete Map of Car-Building Resources</title><link>https://blog.lynxflow.co/en/posts/that-childhood-sports-car-dream-maybe-you-really-can-build-one-yourself/</link><pubDate>Tue, 04 Aug 2026 00:00:00 +0800</pubDate><guid>https://blog.lynxflow.co/en/posts/that-childhood-sports-car-dream-maybe-you-really-can-build-one-yourself/</guid><description>&lt;img src="https://blog.lynxflow.co/files/img/roadster-2008-front.jpg" alt="Featured image of post That Childhood Sports Car Dream—Maybe You Really Can Build One Yourself: The Truth About the Roadster “Open Source” Release and a Complete Map of Car-Building Resources" /&gt;Preface: A Thought That Never Died When I was a kid, I always dreamed of owning a sports car. All those insanely cool machines—McLaren, Lamborghini, Ferrari—with their low-slung bodies, exaggerated wings, and roaring mechanical hearts in the engine bay, were my earliest understanding of the word “cool.” Back then, I felt like a sports car was one of the farthest things in the world from me.
But that thought never died: someday, when I had the money, maybe I really could build a sports car of my</description></item></channel></rss>