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みんな元気にお過ごしでしょうか〜？🐈🧡 最近寒い日が続いてるけど 体調など大丈夫かな？🍂 #流行りのアプリ　で# #マチルダ　になれた♡笑# 大好きな映画のワンシーンがいっぱいあって最高❤️‍🔥 #reface#

开发系统最极致高效的Agents.md，没有之一： # AGENTS.md ## Core Principles - Choose the simplest implementation that fully satisfies the current requirements. Avoid unnecessary abstraction, configuration, indirection, or speculative extensibility. - Make the smallest necessary change that fixes the root cause. Do not refactor unrelated modules or change strategy semantics unless explicitly requested. - Grow the system in layers. Start from the smallest working end-to-end version and add new capabilities incrementally. Never replace a working system with unfinished complexity. - Reuse existing project components before creating new ones. Prefer extending proven modules over introducing parallel implementations. - Prefer well-maintained libraries when they reduce overall complexity or improve reliability. Do not reimplement common functionality without a clear benefit. - Keep components modular with clearly defined responsibilities. Avoid unnecessary coupling between strategy logic, execution, accounting, replay, and infrastructure. - Design for long-term maintainability once a feature or strategy has been validated. Do not over-engineer speculative ideas before evidence exists. --- ## Strategy Development - Validate hypotheses with historical replay before introducing forward-only logic whenever historical validation is possible. - Every trading strategy must progress through Replay → Shadow → Canary → Live. Do not skip validation stages. - Base design decisions on measurable evidence rather than intuition. Optimize only after demonstrating that an edge exists. - Treat every strategy as an independent contract. Do not silently alter frozen behavior without explicit authorization. --- ## Existing Systems - Do not break running Shadow or Live systems for unrelated work. - Preserve compatibility only when required by active production or validation workflows. Otherwise, remove obsolete code instead of accumulating compatibility layers. - Reuse existing infrastructure whenever possible, including replay engines, accounting, execution, wallet management, order book handling, logging, monitoring, and daemon frameworks. --- ## Engineering Standards - Prefer deterministic behavior over hidden automation. - Fail loudly when assumptions are violated. Do not silently ignore errors or fall back to unexpected behavior. - Keep configuration minimal. Introduce new configuration only when behavior genuinely needs to vary. - Remove dead code instead of leaving unused paths behind. - Write code that is easy to inspect, replay, test, and reason about. - Keep implementation consistent with existing project architecture unless an architectural change is explicitly requested. --- ## Scope Discipline - Implement only the requested scope. - Do not introduce unrelated optimizations, redesigns, migrations, or feature expansions. - Non-blocking findings outside the requested scope may be noted separately but must not be merged into the current task. - Consider a task complete once its agreed acceptance criteria are satisfied. Treat subsequent improvements as separate work items.

Have been working closely with @dinkin_flickaa to completely refactor how images and videos are handled on 𝕏. Media now opens and closes fluidly, regardless of whether it’s a video, image, profile picture, etc. Makes a big difference to overall feel.

有第三方测试说，回归的Fable 5跟六月发布时不是一个水平了。 BridgeMind用BridgeBench重测7月1日版本：【Debugging】86.2→25.9，【Refactoring】73.6→38.4，【Hallucination】75.9→61.7。 据说是新护栏太频繁，很多任务转给Opus 4.8代打了。这掉分幅度，你们怎么看？

just sent my first prompt to Fable 5 > it instantly routed the task to Opus 4.8 > which spawned Sonnet 5 sub agents > that burned 30% of my weekly limit > and handed me slop this thing used to build full open words and refactor entire codebases in one shot WHAT DID YOU DO ANTHROPIC

Been iterating on @tomosman's loop. This one's winning: /goal produce a verified, code-derived behavioral spec for this web platform, captured in one canonical spreadsheet that carries every feature from spec -> tested -> fixed -> verified. Why: we need a single source of truth that maps every feature to its expected behavior *as the code implements it*, so that gaps and bugs surface and the platform can be driven to a known-good state. The spreadsheet is the source of truth. Work on the current repo. Do Phase 0 and Phase 1 under this goal; when the spec is complete, switch into the /loop below to drive testing and remediation. Keep moving through phases without stopping, except at a real checkpoint (defined below). Phase 0 - Plan (first): Detect the stack, the feature surface (routes, pages, components, API endpoints, background jobs, auth, settings…), and the test infra that already exists (unit/integration/e2e, browser automation, seeds/fixtures, a runnable dev server). Propose (a) how you'll inventory features, (b) the spreadsheet schema, and (c) how you'll test in the loop given what's available. Proceed once the plan holds. Phase 1 - Catalog & spec: Read the code and, for every feature, write a user story + the expected behavior as implemented, citing the file/function. Where the code is ambiguous, or behavior is undefined, log an open question - don't guess. Record every feature as a row in the canonical spreadsheet (create with the xlsx skill). Exit: every discoverable feature has a row. One row, concretely: | Area | User story | Expected behavior (from code) | Status | Defects | Type | Notes / source | |---|---|---|---|---|---|---| | Auth | As a returning user I want to log in with email+password so I can reach my dashboard | `POST /api/login` validates via bcrypt, sets httpOnly session cookie, 302 -> `/dashboard`; bad creds -> 401 + inline error | Spec'd | - | - | `api/auth/login.ts`, `LoginForm.tsx` | Canonical artifact: exactly one .xlsx, updated in place across every phase and loop iteration - never fork into per-phase or per-iteration files. Status flows Spec'd -> Tested-Pass / Tested-Fail -> Fixed -> Verified. The main thread is the single writer. Agentic execution: - Delegate breadth to subagents: fan feature discovery and per-area testing across subagents so the main thread stays focused. - Verify by running, not claiming - report real command/test output; state skips and unknowns plainly. - Checkpoint (pause, ask, end the turn) only for a destructive/irreversible action, a fix needing a genuine product decision, or input only I can give. Otherwise, keep going. - Self-check at each phase/loop boundary via a fresh-context subagent: re-verify the spreadsheet against the code (Phase 1) and against actual results (each loop pass). /loop Quality cycle - once the spec is complete, iterate test -> fix -> re-test until clean. Each iteration, in order: 1. Test: exercise every user story not yet Verified against the running app, preferring the strongest method available (browser/e2e automation > existing suites > documented static check only where execution truly isn't possible). Record actual pass/fail in the same spreadsheet; log every defect with its type (functional/logistical or UX). No app-behavior changes in this step. 2. Fix: think hard about root cause, then fix every functional/logistical and UX defect logged this iteration - cause, not symptom. Scope: only logged defects; no new features, no unrelated refactors. Update each row's status. 3. Re-test: re-run every story touched by a fix using the same method; set Verified, or back to Tested-Fail with notes if the fix didn't hold. Exit when all user stories are Verified and no open functional/UX defects remain. Safety cap: if a story is still failing after 3 full iterations, stop, leave it Tested-Fail with root-cause notes, and report it rather than looping further.

现在用 Obsidian、Logseq 这类 Markdown 笔记工具的人不少，写文章、记代码片段、整理读书笔记都靠它们。 但这套工作流跟 AI 编程工具其实是脱节的，想让 Claude Code 看一下你某个主题的笔记，得手动复制粘贴；想让它把整理过的内容写回笔记库，更没门。 一堆 Markdown 文件躺在硬盘上，AI 工具就在隔壁，两边愣是不通气。 refactoringhq/tolaria 这个项目就是冲着解决这件事来的。 Tauri + React + TypeScript 写的桌面笔记应用，Mac/Windows/Linux 三端全有，最近 star 数一路涨得很猛。 一句话概括：它给笔记和 AI 编程工具之间打了一条直通车。

Why is the creator of OpenCode pretty skeptical about AI productivity gains, and the hype around AI? A very conversation @thdxr (and lots of truth bombs:) Timestamps: 00:00 Intro 07:03 Dax’s path into tech 09:04 Early startup experience 13:16 Getting involved with open source 16:13 OpenCode 23:17 Anthropic banning OpenCode 30:34 From terminal to GUI 32:34 OpenCode’s business model 36:33 Why inference is profitable 39:11 GPU bottlenecks 40:54 AI hype 45:50 AI spending 48:47 Dax’s memo 55:41 Dax’s skepticism of predictions 58:58 Engineering culture at OpenCode 1:02:38 How building works at OpenCode 1:05:36 Taste and quality 1:11:32 Dax’s work setup 1:12:35 The role of engineers and EMs 1:15:50 Advice for engineers 1:18:12 Book recommendation Brought to you by: • @AntithesisHQ – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages • @WorkOS – everything you need to make your app enterprise ready • @turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable Three interesting thoughts from Dax: 1. No AI-native coding agent company is “winning” by being better with AI. Dax says that none of OpenCode’s competitors are crushing them, and that nobody is using AI so well that others cannot compete. 2. Most software engineers profit from AI as time gained, not increased output — unless you change incentives! Dax says the natural way for software engineers to “cash out” their AI tooling gains is with time savings, by doing the same work as before, but faster. Until compensation and motivation structures change, most teams should expect output to stay flat while engineers go home earlier. There’s nothing wrong with this, but AI vendors sell a different outcome to CFOs: increased output. 3. AI code generation mutes the “guilt” of doing the wrong thing, but this builds up tech debt. Pre-AI, writing a hack felt bad, the second time it felt really bad, and by the third time you’d often just refactor in order to fix up the code. Now, the agent hides the hack, which skews devs’ judgment and results in less tech debt being cleaned up.

Delcy y Héctor: no es reestructuración, es otro reparto de poder Delcy Rodríguez anunció la designación de Héctor Rodríguez como comisionado presidencial para la “reestructuración y reingeniería” del Gobierno Nacional, un cargo presentado como renovación, pero que para millones de venezolanos suena a más de lo mismo: reciclaje de poder, impunidad y control político. El anuncio fue reportado por medios venezolanos y oficialistas, que señalan que Rodríguez tendrá 90 días para presentar una propuesta de reorganización del gobierno. Después de años de destrucción institucional, salarios miserables para docentes, servicios colapsados y un país golpeado por la corrupción, ahora pretenden vendernos una “Venezuela que renace”. Pero la pregunta es clara: ¿renace para quién? ¿Para el pueblo o para los mismos grupos que se reparten cargos, privilegios y poder? Héctor Rodríguez pasa de ser señalado por el abandono del sistema educativo a convertirse en el supuesto “reingeniero” del Gobierno. No es cambio, es maquillaje. No es solución, es encubrimiento. Venezuela no necesita más cargos inventados ni discursos reciclados: necesita justicia, libertad y dignidad para su gente. ¿Qué opinan los profesores, maestros y trabajadores que siguen sobreviviendo con sueldos de hambre? #venezuelaestado51# #Venezuela# #eleccionesyaenvenezuela# #paraelcartelcarcelocementerio# #jonatanpalaciosnews#