Tim Nguyen

[Hany @hany99dev]

Tibo says more resets are coming this week but honestly the last one did nothing for many users including me except push our weekly reset date back.

I barely used Codex last week because I was on Opus 5.5 most of the time so I still had most of my usage left and the reset didn't give me anything extra and just moved my reset date by another 7 days.

I think it would be much better if users who have 50% or less of their weekly usage left get the reset right away and users who still have more than 50% left get a banked reset they can use when they actually need it.

Right now a reset for someone who barely used Codex is not a gift but just a delay.

[Tim Nguyen @TimNguyen2013]

@ildoraStudio @dextune It’s also about the quality and speed. Sometime it’s fast but keep failing in the loop and take so long, I have use about 3B token of minimax so I can feel it

[Tim Nguyen @TimNguyen2013]

@dextune @ildoraStudio No the coding is not good

[Charlie Hills @charliejhills]

Claude Opus 5.5 can (finally) generate motion graphics

16 motion graphics in Claude Code (my 3 steps):

1. Pick one motion, like a chart that morphs.
2. Show Claude a reference and name every state.
3. Ask for HTML and SVG, then fix it round by round.

Download them all here:

https://t.co/pjMv8ExvWx

Repost ♻️ to help someone in your network.

[Tim Nguyen @TimNguyen2013]

@thsottiaux I just use the last reset now to told me that you gonna reset :)

[Tim Nguyen @TimNguyen2013]

@thsottiaux Pls don’t remove 5.6 sol,6 sol is not good

[Hanako @hanakoxbt]

https://t.co/5w8ILIIelz

[Callan @callanxai]

Google Brain founder, Andrew Ng:

"Prompting will die in 6 months. Loops and graphs are what's replacing it."

In 97 minutes he shows how to build agents that plan, execute and improve without you

Prompts → Agents → Loops → Graphs

a loop closes one job without you sitting there

a graph decides which jobs exist and carries every accepted one into the next run

most people are still getting better at the part he gives six months

watch it today, then save the full guide on loops and graphs below ↓

[zero @twoclipping]

opus 5.5 is f*cking cracked at motion design

this entire video is code, 0 after effects

im open sourcing the prompt template for these motion designs

steal it to recreate these ↓

<inputs>
Ask me for: 8 to 12 UI states I want the shape to become (e.g. button, loader, player, slider, toggle, tabs, chart, command palette, toast), pure black and white or one accent color, and a royalty-free song around 120 BPM (e.g. Mixkit, free for commercial use).
</inputs>

<direction>
Dribbble-level UI motion. One shape, never cut: every state is the same element morphing its size, radius and color while its content swaps with a short blur. A cursor drives every change with real clicks and drags. Light warm-gray canvas, black and white components, one clean UI font (Geist). Springs everywhere, a tiny overshoot at most. The camera zooms so each state fills the frame. The last frame is the first frame, so it loops.
Banned: bouncy easing, particle bursts, glows, gradients on UI chrome, mismatched icon strokes, dead time, anything that looks like a template.
</direction>

<structure>
120 BPM, 7 bars, something happens on every beat.
Button → loader → check → dynamic island → music player with a play/pause morph → scrub the progress bar → it becomes a volume slider that stretches when dragged past max → a toggle flips on the beat → the knob becomes a liquid tab indicator → the tabs open into a chart that draws itself, with a tooltip on hover → it collapses into ⌘K → type to filter → enter → toast → back to the button.
</structure>

<build>
1. One HTML file, square 1440x1440. Every style is computed from time inside seek(t): no CSS transitions, no timers, no state carried between frames.
2. Springs are closed-form step responses. A value that changes target many times is the sum of one spring per change, so it stays a pure function of time.
3. The tab indicator's two edges ride different springs, so the leading edge stretches ahead of the trailing one. Same trick for the toggle knob.
4. Drags are direct manipulation: while the cursor is held, the value is computed from its position. On release it springs back from wherever it was.
5. Analyze the song with numpy for the beat grid and start on a downbeat. Place every UI sound by its measured peak.
6. Render with Playwright: 4 subframes per frame, blended with ffmpeg tmix for motion blur at 60fps.
7. Render one frame per beat before the full render. Fix anything off the grid, cramped or hard to read.
</build>

<gotchas>
Never put will-change on anything the camera scales or the text renders blurry. Text that swaps inside a morphing container needs its own enter and exit timing or it overlaps. Make the last frame identical to the first, cursor position and speed included, or the loop stutters.
</gotchas>

<start>
Ask me for the inputs, then show me the state list on the beat grid before you write any code.
</start>

[Himanshu Hingorani @himanshubuildss]

Created this in just 10 min using GPT-6 Astra

100% prompts, zero code written

the stack:

- built entirely in Codex
- 90% GPT-6 Astra, rest GPT-5.6 Sol
- switched to Sol for speed and cost
- art generated with ChatGPT Images 2.5

Let me know your thoughts on this?

Prompt 👇

[Daniel White @dwhitedesign]

Made a launch film for my animation library.

Every frame is an SVG drawn in JavaScript. No After Effects, no WebGL.

https://t.co/xCDlmoFvjZ

[lumxss @bkdgiffug]

程序员最容易卡住的，有时候不是代码，而是 UI 审美。

我的懒人玩法：找现成的漂亮组件，直接丢给 AI Agent，让它自己挑、自己改、自己塞进项目。

这 5 个站先收藏：

1️⃣ Beautiful UI — https://t.co/Xd7RxNXeK1
2️⃣ BeUI — https://t.co/EHKEVhoZhy
3️⃣ Rare UI — https://t.co/ZJ2ymEocbT
4️⃣ Transitions — https://t.co/Loc08STWGc
5️⃣ shadcn/ui — https://t.co/szLxAsJUHt
Claude Code、Codex 这类 Agent 都能拿来参考。
不会设计没关系，先知道好东西去哪找。

[Jason Young @Jason_Young1231]

Kimi Code 桌面版正式发布了，初步尝试了一下，完成度很高，该有的功能基本上都有，UI 设计依旧很有 taste，内置了非常独特且有价值的 Kimi Data Source 插件，建议安装

[Tim Nguyen @TimNguyen2013]

@Ronny_MiniMax Please improve the quality for coding

[RonnyHe @Ronny_MiniMax]

/goal Audit this project for actionable performance, resource-management, and concurrency problems. Identify unnecessary work, poor scaling, resource retention, and lifecycle errors that affect meaningful user or system workflows. Validate findings with reproducible evidence and rank them by expected repair ROI.

Do not assume a particular language, framework, architecture, or application type. Adapt the investigation to the repository.

## 1. Discover the system

Read the repository's instructions and identify:

- Its purpose, architecture, execution environments, and supported platforms.
- Critical user journeys or system operations.
- Entry points, data flows, persistence boundaries, background jobs, and external dependencies.
- Existing tests, benchmarks, profiling tools, and observability.
- Representative workload sizes and any documented performance requirements.

Record the reviewed revision, working tree state, and environment.

Select a bounded set of important workflows based on impact and likely risk. Explain the selection. Review the current implementation, not only recent changes.

## 2. Investigate how work and resources scale

Trace selected workflows end to end. Look for patterns such as:

- Small or bounded requests triggering full scans, large materialization, or complete recomputation.
- Repeated queries, network calls, serialization, parsing, rendering, or filesystem operations.
- N+1 access patterns, quadratic algorithms, repeated collection copies, and redundant dependency resolution.
- Blocking work on latency-sensitive threads or event loops.
- Excessive startup work, eager initialization, and avoidable work on frequently executed paths.
- Missing backpressure, excessive concurrency, retry amplification, and unbounded queues.
- Caches whose memory grows without an effective total budget, invalidation policy, or eviction strategy.
- Leaked or unnecessarily retained subscriptions, listeners, timers, handles, connections, tasks, and subprocesses.
- Timeouts that do not cancel underlying work, abandoned requests that keep running, and asynchronous work that outlives its owner.
- Lock contention, overly broad serialization, races, stale writes, and inconsistent read-modify-write operations.
- Polling, watchers, refresh loops, or invalidation chains that amplify small changes into large amounts of work.

For each path, ask:
“What determines its cost: request size, total stored data, elapsed uptime, concurrency, or unrelated system activity?”

Treat suspicious patterns as hypotheses. A pattern alone is not a defect.

## 3. Validate promising hypotheses

For each candidate:

1. Identify a realistic trigger and the expected behavior or resource bound.
2. Inspect callers, guards, caching, cancellation, and cleanup before drawing conclusions.
3. Create the smallest reproducible experiment using the actual implementation.
4. Prefer realistic local dependencies and isolated fixtures over mocks.
5. Use controlled stubs only where necessary, and state exactly what they replace.
6. Vary input size, accumulated state, or concurrency to establish scaling.
7. Measure the relevant quantities: operation counts, rows, bytes, allocations, retained resources, CPU, elapsed time, latency distribution, or throughput.
8. Include controls and repeated samples where meaningful.
9. Separate instrumentation overhead, warm-up, caching, and environmental noise from application cost.

Do not equate high resource usage with waste without understanding its purpose. Do not present synthetic benchmark timing as observed production latency.

Run focused existing tests and appropriate profiling tools. Avoid broad test runs unless they answer a concrete question.

Investigate failures instead of repeatedly rerunning until green. Distinguish product defects, cascading failures, test isolation problems, and environment limitations.

## 4. Exercise the real interface

Where feasible, validate the affected workflow through the project's actual interface: application UI, CLI, API, worker, service, library consumer, or batch job.

Confirm which build and configuration are running.

Keep these evidence levels distinct:
- Static code analysis.
- Isolated implementation probes.
- Integration tests.
- Real interface or system workflows.
- Production observations, if available and authorized.

If access, credentials, tooling, or infrastructure blocks validation, continue independent work and document the exact limitation. Never substitute a fixture or mock and describe it as end-to-end validation.

## 5. Rank actionable findings

For each confirmed finding, provide:

- Severity and confidence.
- Exact source locations.
- Triggering conditions and affected workflows.
- Expected versus observed behavior.
- Reproduction steps and raw evidence.
- Scaling behavior and practical impact.
- A minimal repair direction.
- Regression acceptance criteria.
- Remaining uncertainty.

Rank repair ROI using:
- Impact on users or system reliability.
- Likely frequency and affected population.
- Strength of evidence.
- Expected implementation effort.
- Regression and operational risk.

Label unknown frequency and repair cost as estimates. Avoid false numerical precision.

Prefer removing unnecessary work over weakening guarantees. Do not automatically recommend more caching, batching, concurrency, lower limits, or broader architectural changes.

## 6. Boundaries and completion

Follow repository-specific instructions. Keep tracked product code unchanged unless fixes are explicitly requested. Temporary probes, isolated fixtures, profiles, and reports are allowed.

Do not modify real user data, disrupt unrelated processes, use production systems without authorization, or publish sensitive evidence.

Deliver:
- An executive summary and prioritized findings.
- A workflow coverage matrix.
- Reproduction artifacts and measurements.
- Rejected hypotheses.
- Unresolved risks and validation blockers.

Finish when the selected workflows have been investigated or have explicit blockers, every confirmed finding has a defensible evidence chain, and the report is ready for review.

Do not stop at a list of suspicious code patterns. Do not invent findings to meet a quota. Do not claim to have found every possible problem.

[Tim Nguyen @TimNguyen2013]

@ashen_one Can I ask where you use it from ? Kimi app or other harness?

[Tim Nguyen @TimNguyen2013]

@thsottiaux Please reset

[Lonely @Lonely__MH]

🎉免费个人域名邮箱终极方案！

Cloudflare + Gmail + Resend 强强联手！

- Cloudflare：免费收信转发（支持 Catch-all）
- Resend：免费 SMTP 发信（每月 3000 封）
- Gmail：统一收发

全程配置5分钟搞定✌🏻

[shogo kajiyama @kebura_P]

FFmpeg Skill Update 🎬

Ver.0.9.2→Ver.0.12.0

「この動画を9:16にして」「無音カットして」「字幕入れて」
に加えて、今はこんな指示もできます。

「この部分だけピクセル単位でクロップして」
「この写真、ズームしながらクリップにして」(Ken Burns風)
「動画を回転/反転して」
「この動画にロゴ動画を合成して」「グリーンバックを抜いて」
「この動画、逆再生にして」
「手ブレを直して」
「連番の画像から動画作って」
「単色/グラデーションの背景クリップ作って」
「劣化なしでそのまま書き出して」
「字幕は画質そのままで、後から切り替えられる形で入れて」
「ハイライトは音量じゃなくて長さで選んで」
「AI解析用に軽い解像度でプレビュー書き出して」

できることが21→28個のツールに増えました。

🔍 AIが「わかった上で」処理するように

・「今この環境でこのツールが実際に使えるか」を実行前に事前チェック
・GPUエンコーダー/フォント代替の有無も把握
・複数の音声トラックを持つファイル(吹き替え・別言語版など)で、使うトラックをほぼ全ツールで指定可能に
・字幕トラックの言語などの詳細も読み取れるように
・字幕タイミングをタイムコード(hh:mm:ss:ff)形式でも指定可能に
・9:16化などのクロップで、切り取る位置(左寄せ/右寄せなど)も指定可能に
・音声を再エンコードするかしないか、各ツールが正直に申告するように

✅ 「壊れない・嘘をつかない」を徹底

・書き出しが本当に成功したか毎回確認してから「完了」と報告、失敗時のゴミファイルも残さない
・元の動画ファイルを誤って上書き・破損させない安全策
・失敗した時、原因とやり直せるかをAIが判別
・色変換で再エンコードが必要になった場合も、字幕や追加の音声トラックをできる限り残すように
・同期のズレを検出した時、信頼度が低い場合はAIに警告
・最新FFmpeg / Windows環境でも安定動作(Windowsのパス周りのバグ修正含む)

🛠️ 開発・保守の仕組みも強化

・「AIがして良いこと/判断すべきでないこと」を明文化(勝手にAIが「見た目の良さ」を判断しない設計)
・生FFmpegコマンドへの逃げ道を塞ぐテストを追加
・ドキュメントと実際のコードの挙動がズレたら自動テストで検知
・OSSとしてのContributing手順を整備、バージョンタグを打つとGitHub Releaseが自動生成される仕組みを追加
・GPU対応について「対応してるとは言うが動作保証はしない」ことを正直に明記

Claude Code / Cursor / Codex対応、MCPあり、ローカル処理でクラウド不要は変わらず。

Current: v0.12.0

https://t.co/5Uuz4Zs09B

#FFmpeg #AIVideo #AI #MCP #ClaudeCode #OpenSource

[Higgsfield AI 🧩 @higgsfield_ai]

GPT-6 Astra just solved data labelling.

13,038 annotations across 81 frames of parcel-sorting warehouse CCTV generated with Higgsfield.

The use cases are endless: track parcel handoffs, monitor worker movement with cart traffic, and spot bottlenecks.