JcValentine

Software Engineer | Health Informatics specialist

[Ruben Hassid @rubenhassid]

https://t.co/O0jpyx3nCZ

[Vishakha Singhal @vishisinghal_]

🚨 Breaking : Anthropic engineers are quietly using a simple trick that most AI users completely ignore.

It’s called “Context Stacking.”

Instead of jumping straight to the prompt…

They build the context first - step by step.

Most people do this:

❌ Ask → Get answer → Retry → Repeat

But advanced AI builders follow a structured flow:

Step 1: Set the role
Tell the AI who it should act like
“Act like a senior product manager…”

Step 2: Define constraints
Guide how it should think
“Focus on retention, avoid generic ideas…”

Step 3: Add detailed context
Give all the background it needs
“Here’s the product, users, and current problem…”

Step 4: Provide examples (optional but powerful)
Show what good output looks like

Step 5: Ask the actual task
“Now suggest 3 high-impact solutions”

Now compare the difference 👇

❌ Random prompt → average output
✅ Structured context → high-quality output

Because AI doesn’t just depend on your question…

It depends on the setup before the question.

The result?

• more accurate answers
• better reasoning
• fewer retries

Most people are still stuck in:

→ Prompting

But top builders are moving towards:

→ Context Engineering

Same model.
Different approach.
Massive difference 🚀

🔁 Repost to share this with AI builders

Follow @vishisinghal_ for more practical AI workflows & insights

[Ruben Hassid @rubenhassid]

How to climb all 4 layers of Claude in one weekend:

(even if you just signed up yesterday)

✦ Level 1: Claude Chat (Saturday morning)

Go to claude .com/download. Install the app.
Pay the $20. Select Opus 4.6 + Extended Thinking.
Connect Slack, Drive, Notion through Connectors.
Stop writing long prompts. Prompt this instead:
"I want to [TASK] for [SUCCESS CRITERIA]. Use AskUserQuestion before you start."

Most people stay here forever.
You're leaving 90% of Claude untouched.
Claude Basics: https://t.co/jw2qdIbLxJ.

——

✦ Level 2: Claude Cowork (Saturday afternoon)

Go to 'Cowork'. Create a folder "Claude-Cowork."
4 subfolders. About me, template, project & outputs
Create about-me .md: what you do, how you do.
Create anti-ai-style .md: words you'd never say.
Set Global Instructions (Settings → Cowork → Edit):
"Always read my files first, never edit my originals, deliver everything to CLAUDE OUTPUTS."

You just killed prompting.
From now on, your prompt is 2 lines + your folder.
Move on to Cowork: https://t.co/uWTpOI3oyE

——

✦ Level 3: Skills + Plugins (Sunday morning)

Open Cowork. Type: "Use the skill-creator to help me build a skill for [your most repeated task]."
Claude interviews you. Answer and be specific.
It generates a SKILL .md. Test it: "When would you use this skill?" If the description is vague, fix it.
Upload: Settings → Capabilities → Skills → Upload.
Now it fires automatically. No slash command.
Install Plugins: Cowork > Customize > Browse plugin

Skills with about-me .md. Skill handles the process.
Voice file handles tone. Two layers simultaneously.
Set up Claude Skills: https://t.co/SAErc3JcLL

——

✦ Level 4: Code + Computer (Sunday afternoon)

Click the Code tab. Create a folder. Connect GitHub
(free account → Settings → Connectors).
Prompt: "Create a GitHub repo named [project]. Code everything. Don't ask for permissions."
Download VS Code. Install the Claude extension.
Turn on "Skip Permissions" to go 100x faster.
After your session, paste this: "Create a CLAUDE .md file with everything you learned about this project." Now Claude remembers your fonts, colors, and structure forever.
Claude code guide: https://t.co/UgE9xBXnm6

Claude Computer: Settings → Desktop app → turn on Browser use + Computer use.
Connect your phone with Dispatch. Text a task.
Schedule a recurring task: left sidebar → Scheduled → write the prompt → pick the frequency.
Claude Computer: https://t.co/ZfjFaaMknc

This is where you stop working inside Claude.
Claude starts working on your computer.

——

Saturday, you were prompting like it's ChatGPT.
Sunday, Claude is running your screen, building your website, & sending you text updates.

That's all 4 layers. In one weekend.

[Khairallah AL-Awady @eng_khairallah1]

https://t.co/5UG1H5Ywbf

[Andrej Karpathy @karpathy]

LLM Knowledge Bases

Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So:

Data ingest:
I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them.

IDE:
I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides).

Q&A:
Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale.

Output:
Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base.

Linting:
I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into.

Extra tools:
I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries.

Further explorations:
As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows.

TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

[Georgi Gerganov @ggerganov]

Let me demonstrate the true power of llama.cpp:

- Running on Mac Studio M2 Ultra (3 years old)
- Gemma 4 26B A4B Q8_0 (full quality)
- Built-in WebUI (ships with llama.cpp)
- MCP support out of the box (web-search, HF, github, etc.)
- Prompt speculative decoding

The result: 300t/s

(realtime video)

[Google @Google]

We just released Gemma 4 — our most intelligent open models to date.

Built from the same world-class research as Gemini 3, Gemma 4 brings breakthrough intelligence directly to your own hardware for advanced reasoning and agentic workflows.

Released under a commercially permissive Apache 2.0 license so anyone can build powerful AI tools. 🧵↓

[BuBBliK @k1rallik]

https://t.co/cBzM6ndCfb

[Google Research @GoogleResearch]

Introducing TurboQuant: Our new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency. Read the blog to learn how it achieves these results: https://t.co/CDSQ8HpZoc

[Fuli Luo @_LuoFuli]

MiMo-V2-Pro & Omni & TTS is out. Our first full-stack model family built truly for the Agent era.

I call this a quiet ambush — not because we planned it, but because the shift from Chat to Agent paradigm happened so fast, even we barely believed it. Somewhere in between was a process that was thrilling, painful, and fascinating all at once.

The 1T base model started training months ago. The original goal was long-context reasoning efficiency. Hybrid Attention carries real innovation, without overreaching — and it turns out to be exactly the right foundation for the Agent era. 1M context window. MTP inference for ultra-low latency and cost. These architectural decisions weren't trendy. They were a structural advantage we built before we needed it.

What changed everything was experiencing a complex agentic scaffold — what I'd call orchestrated Context — for the first time. I was shocked on day one. I tried to convince the team to use it. That didn't work. So I gave a hard mandate: anyone on MiMo Team with fewer than 100 conversations tomorrow can quit. It worked. Once the team's imagination was ignited by what agentic systems could do, that imagination converted directly into research velocity.

People ask why we move so fast. I saw it firsthand building DeepSeek R1. My honest summary:

— Backbone and Infra research has long cycles. You need strategic conviction a year before it pays off.
— Posttrain agility is a different muscle: product intuition driving evaluation, iteration cycles compressed, paradigm shifts caught early.
— And the constant: curiosity, sharp technical instinct, decisive execution, full commitment — and something that's easy to underestimate: a genuine love for the world you're building for.

We will open-source — when the models are stable enough to deserve it.

From Beijing, very late, not quite awake.

[Todd Saunders @toddsaunders]

Got flooded with DMs asking for the markdown file.

Here it is, bookmark this.

---
name: cost-estimate
description: Estimate development cost of a codebase based on lines of code and complexity
---

# Cost Estimate Command

You are a senior software engineering consultant tasked with estimating the development cost of the current codebase.

## Step 1: Analyze the Codebase

Read the entire codebase to understand:
- Total lines of code (Swift, C++, Metal shaders)
- Architectural complexity (frameworks, integrations, APIs)
- Advanced features (Metal rendering, CoreMediaIO, AVFoundation)
- Testing coverage
- Documentation quality

Use the Glob and Read tools to systematically review:
- All Swift source files in Sources/
- All C++ files in DALPlugin/
- All Metal shader files
- All test files in Tests/
- Build scripts and configuration files

## Step 2: Calculate Development Hours

Based on industry standards for a **senior full-stack developer** (5+ years experience):

**Hourly Productivity Estimates**:
- Simple CRUD/UI code: 30-50 lines/hour
- Complex business logic: 20-30 lines/hour
- GPU/Metal programming: 10-20 lines/hour
- Native C++ interop: 10-20 lines/hour
- Video/audio processing: 10-15 lines/hour
- System extensions/plugins: 8-12 lines/hour
- Comprehensive tests: 25-40 lines/hour

**Additional Time Factors**:
- Architecture & design: +15-20% of coding time
- Debugging & troubleshooting: +25-30% of coding time
- Code review & refactoring: +10-15% of coding time
- Documentation: +10-15% of coding time
- Integration & testing: +20-25% of coding time
- Learning curve (new frameworks): +10-20% for specialized tech

**Calculate total hours** considering:
1. Base coding hours (lines of code / productivity rate)
2. Multipliers for complexity and overhead
3. Phases completed vs. remaining
4. Specialized knowledge required (CoreMediaIO, Metal, etc.)

## Step 3: Research Market Rates

Use WebSearch to find current 2025 hourly rates for:
- Senior full-stack developers (5-10 years experience)
- Specialized iOS/macOS developers
- Contractors vs. employees
- Geographic variations (US markets: SF Bay Area, NYC, Austin, Remote)

Search queries to use:
- "senior full stack developer hourly rate 2025"
- "macOS Swift developer contractor rate 2025"
- "senior software engineer hourly rate United States 2025"
- "iOS developer freelance rate 2025"

## Step 4: Calculate Organizational Overhead

Real companies don't have developers coding 40 hours/week. Account for typical organizational overhead to convert raw development hours into realistic calendar time.

**Weekly Time Allocation for Typical Company**:

| Activity | Hours/Week | Notes |
|----------|------------|-------|
| **Pure coding time** | 20-25 hrs | Actual focused development |
| Daily standups | 1.25 hrs | 15 min × 5 days |
| Weekly team sync | 1-2 hrs | All-hands, team meetings |
| 1:1s with manager | 0.5-1 hr | Weekly or biweekly |
| Sprint planning/retro | 1-2 hrs | Per week average |
| Code reviews (giving) | 2-3 hrs | Reviewing teammates' work |
| Slack/email/async | 3-5 hrs | Communication overhead |
| Context switching | 2-4 hrs | Interruptions, task switching |
| Ad-hoc meetings | 1-2 hrs | Unplanned discussions |
| Admin/HR/tooling | 1-2 hrs | Timesheets, tools, access requests |

**Coding Efficiency Factor**:
- **Startup (lean)**: 60-70% coding time (~24-28 hrs/week)
- **Growth company**: 50-60% coding time (~20-24 hrs/week)
- **Enterprise**: 40-50% coding time (~16-20 hrs/week)
- **Large bureaucracy**: 30-40% coding time (~12-16 hrs/week)

**Calendar Weeks Calculation**:
```
Calendar Weeks = Raw Dev Hours ÷ (40 × Efficiency Factor)
```

Example: 3,288 raw dev hours at 50% efficiency = 3,288 ÷ 20 = **164.4 weeks** (~3.2 years)

## Step 5: Calculate Full Team Cost

Engineering doesn't ship products alone. Calculate the fully-loaded team cost including all supporting roles.

**Supporting Role Ratios** (expressed as ratio to engineering hours):

| Role | Ratio to Eng Hours | Typical Rate | Notes |
|------|-------------------|--------------|-------|
| Product Management | 0.25-0.40× | $125-200/hr | PRDs, roadmap, stakeholder mgmt |
| UX/UI Design | 0.20-0.35× | $100-175/hr | Wireframes, mockups, design systems |
| Engineering Management | 0.12-0.20× | $150-225/hr | 1:1s, hiring, performance, strategy |
| QA/Testing | 0.15-0.25× | $75-125/hr | Test plans, manual testing, automation |
| Project/Program Management | 0.08-0.15× | $100-150/hr | Schedules, dependencies, status |
| Technical Writing | 0.05-0.10× | $75-125/hr | User docs, API docs, internal docs |
| DevOps/Platform | 0.10-0.20× | $125-200/hr | CI/CD, infra, deployments |

**Team Composition by Company Stage**:

| Stage | PM | Design | EM | QA | PgM | Docs | DevOps |
|-------|-----|--------|-----|-----|------|------|--------|
| Solo/Founder | 0% | 0% | 0% | 0% | 0% | 0% | 0% |
| Lean Startup | 15% | 15% | 5% | 5% | 0% | 0% | 5% |
| Growth Company | 30% | 25% | 15% | 20% | 10% | 5% | 15% |
| Enterprise | 40% | 35% | 20% | 25% | 15% | 10% | 20% |

**Full Team Multiplier**:
- **Solo/Founder**: 1.0× (just engineering)
- **Lean Startup**: ~1.45× engineering cost
- **Growth Company**: ~2.2× engineering cost
- **Enterprise**: ~2.65× engineering cost

**Calculation**:
```
Full Team Cost = Engineering Cost × Team Multiplier
```

Example: $500K engineering cost at Growth Company = $500K × 2.2 = **$1.1M total team cost**

## Step 6: Generate Cost Estimate

Provide a comprehensive estimate in this format:

---

## KeyMe MVP - Development Cost Estimate

**Analysis Date**: [Current Date]
**Codebase Version**: [From CLAUDE.md phase status]

### Codebase Metrics

- **Total Lines of Code**: [number]
- Swift: [number] lines
- C++: [number] lines
- Metal Shaders: [number] lines
- Tests: [number] lines
- Documentation: [number] lines

- **Complexity Factors**:
- Advanced frameworks: [list key ones]
- System-level programming: [Camera Extensions, DAL Plugins, etc.]
- GPU programming: [Metal shaders, rendering]
- Third-party integrations: [OpenAI, etc.]

### Development Time Estimate

**Base Development Hours**: [number] hours
- Phase 1 (Foundation): [hours] hours
- Phase 2 (Virtual Camera): [hours] hours
- Phase 3 (Audio/Transcription): [hours] hours
- Remaining phases: [hours] hours

**Overhead Multipliers**:
- Architecture & Design: +[X]% ([hours] hours)
- Debugging & Troubleshooting: +[X]% ([hours] hours)
- Code Review & Refactoring: +[X]% ([hours] hours)
- Documentation: +[X]% ([hours] hours)
- Integration & Testing: +[X]% ([hours] hours)
- Learning Curve: +[X]% ([hours] hours)

**Total Estimated Hours**: [number] hours

### Realistic Calendar Time (with Organizational Overhead)

| Company Type | Efficiency | Coding Hrs/Week | Calendar Weeks | Calendar Time |
|--------------|------------|-----------------|----------------|---------------|
| Solo/Startup (lean) | 65% | 26 hrs | [X] weeks | ~[X] months |
| Growth Company | 55% | 22 hrs | [X] weeks | ~[X] years |
| Enterprise | 45% | 18 hrs | [X] weeks | ~[X] years |
| Large Bureaucracy | 35% | 14 hrs | [X] weeks | ~[X] years |

**Overhead Assumptions**:
- Standups, team syncs, 1:1s, sprint ceremonies
- Code reviews (giving), Slack/email, ad-hoc meetings
- Context switching, admin/tooling overhead

### Market Rate Research

**Senior Full-Stack Developer Rates (2025)**:
- Low end: $[X]/hour (remote, mid-level market)
- Average: $[X]/hour (standard US market)
- High end: $[X]/hour (SF Bay Area, NYC, specialized)

**Recommended Rate for This Project**: $[X]/hour

*Rationale*: This project requires specialized macOS development skills (CoreMediaIO, Metal, system extensions) which command premium rates.

### Total Cost Estimate

| Scenario | Hourly Rate | Total Hours | **Total Cost** |
|----------|-------------|-------------|----------------|
| Low-end | $[X] | [hours] | **$[X,XXX]** |
| Average | $[X] | [hours] | **$[X,XXX]** |
| High-end | $[X] | [hours] | **$[X,XXX]** |

**Recommended Estimate (Engineering Only)**: **$[X,XXX] - $[X,XXX]**

### Full Team Cost (All Roles)

| Company Stage | Team Multiplier | Engineering Cost | **Full Team Cost** |
|---------------|-----------------|------------------|-------------------|
| Solo/Founder | 1.0× | $[X] | **$[X]** |
| Lean Startup | 1.45× | $[X] | **$[X]** |
| Growth Company | 2.2× | $[X] | **$[X]** |
| Enterprise | 2.65× | $[X] | **$[X]** |

**Role Breakdown (Growth Company Example)**:

| Role | Hours | Rate | Cost |
|------|-------|------|------|
| Engineering | [X] hrs | $[X]/hr | $[X] |
| Product Management | [X] hrs | $[X]/hr | $[X] |
| UX/UI Design | [X] hrs | $[X]/hr | $[X] |
| Engineering Management | [X] hrs | $[X]/hr | $[X] |
| QA/Testing | [X] hrs | $[X]/hr | $[X] |
| Project Management | [X] hrs | $[X]/hr | $[X] |
| Technical Writing | [X] hrs | $[X]/hr | $[X] |
| DevOps/Platform | [X] hrs | $[X]/hr | $[X] |
| **TOTAL** | **[X] hrs** | | **$[X]** |

### Grand Total Summary

| Metric | Solo | Lean Startup | Growth Co | Enterprise |
|--------|------|--------------|-----------|------------|
| Calendar Time | [X] | [X] | [X] | [X] |
| Total Human Hours | [X] | [X] | [X] | [X] |
| **Total Cost** | **$[X]** | **$[X]** | **$[X]** | **$[X]** |

### Assumptions

1. Rates based on US market averages (2025)
2. Full-time equivalent allocation for all roles
3. Includes complete implementation of MVP features
4. Does not include:
- Marketing & sales
- Legal & compliance
- Office/equipment
- Hosting/infrastructure
- Ongoing maintenance post-launch

### Comparison: AI-Assisted Development

**Estimated time savings with Claude Code**: [X]%
**Effective hourly rate with AI assistance**: ~$[X]/hour equivalent productivity

## Step 7: Calculate Claude ROI — Value Per Claude Hour

This is the most important metric for understanding AI-assisted development efficiency. It answers: **"What did each hour of Claude's actual working time produce?"**

### 7a: Determine Actual Claude Clock Time

**Method 1: Git History (preferred)**

Run `git log --format="%ai" | sort` to get all commit timestamps. Then:
1. **First commit** = project start
2. **Last commit** = current state
3. **Total calendar days** = last - first
4. **Cluster commits into sessions**: group commits within 4-hour windows as one session
5. **Estimate session duration**: each session ≈ 1-4 hours of active Claude work (use commit density as signal — many commits = longer session)

**Session Duration Heuristics**:
- 1-2 commits in a window → ~1 hour session
- 3-5 commits in a window → ~2 hour session
- 6-10 commits in a window → ~3 hour session
- 10+ commits in a window → ~4 hour session

**Method 2: File Modification Timestamps (no git)**

Use `find . -name "*.ts" -o -name "*.swift" -o -name "*.py" | xargs stat -f "%Sm" | sort` to get file mod times. Apply same session clustering logic.

**Method 3: Fallback Estimate**

If no reliable timestamps, estimate from lines of code:
- Assume Claude writes 200-500 lines of meaningful code per hour (much faster than humans)
- Claude active hours ≈ Total LOC ÷ 350

### 7b: Calculate Value per Claude Hour

```
Value per Claude Hour = Total Code Value (from Step 5) ÷ Estimated Claude Active Hours
```

Calculate across scenarios:

| Code Value Scenario | Claude Hours (est.) | Value per Claude Hour |
|--------------------|--------------------|-----------------------|
| Engineering only (avg) | [X] hrs | **$[X,XXX]/hr** |
| Full team equivalent (Growth Co) | [X] hrs | **$[X,XXX]/hr** |
| Full team equivalent (Enterprise) | [X] hrs | **$[X,XXX]/hr** |

### 7c: Claude Efficiency vs. Human Developer

**Speed Multiplier**:
```
Speed Multiplier = Human Dev Hours ÷ Claude Active Hours
```

Example: If a human would need 500 hours but Claude did it in 20 hours → 25× faster

**Cost Efficiency**:
```
Human Cost = Human Hours × $150/hr
Claude Cost = Subscription ($20-200/month) + API costs (estimate from project size)
Savings = Human Cost - Claude Cost
ROI = Savings ÷ Claude Cost
```

### 7d: Output Format

Add this section to the final report:

---

### Claude ROI Analysis

**Project Timeline**:
- First commit / project start: [date]
- Latest commit: [date]
- Total calendar time: [X] days ([X] weeks)

**Claude Active Hours Estimate**:
- Total sessions identified: [X] sessions
- Estimated active hours: [X] hours
- Method: [git clustering / file timestamps / LOC estimate]

**Value per Claude Hour**:

| Value Basis | Total Value | Claude Hours | $/Claude Hour |
|-------------|-------------|--------------|---------------|
| Engineering only | $[X] | [X] hrs | **$[X,XXX]/Claude hr** |
| Full team (Growth Co) | $[X] | [X] hrs | **$[X,XXX]/Claude hr** |

**Speed vs. Human Developer**:
- Estimated human hours for same work: [X] hours
- Claude active hours: [X] hours
- **Speed multiplier: [X]×** (Claude was [X]× faster)

**Cost Comparison**:
- Human developer cost: $[X] (at $150/hr avg)
- Estimated Claude cost: $[X] (subscription + API)
- **Net savings: $[X]**
- **ROI: [X]×** (every $1 spent on Claude produced $[X] of value)

**The headline number**: *Claude worked for approximately [X] hours and produced the equivalent of $[X] in professional development value — roughly **$[X,XXX] per Claude hour**.*

---

---

## Notes

Present the estimate in a clear, professional format suitable for sharing with stakeholders. Include confidence intervals and key assumptions. Highlight areas of highest complexity that drive cost.

[Ashpreet Bedi @ashpreetbedi]

https://t.co/p7ZTwzw29n

[Mckay Wrigley @mckaywrigley]

the claude agent sdk will do for knowledge work in 2026 what claude code did for coding in 2025.

want to live in the future?

build your own agents with it.

you’ll have a personal magic tool belt that the public won’t have for another 6-12mo.

it 100% feels like personal agi.

[Jaya Gupta @JayaGup10]

https://t.co/uPXcTUEsnc

[Rohun ⛳️ @RohunJauhar]

for any CEO using claude code — here's a single prompt that builds your entire 2026 personal productivity system.

annual planning, weekly reviews, etc.. one-shot copy/paste, come back 1 hour later, and start using immediately.
__ __
I want you to autonomously build a PERSONAL PRODUCTIVITY SYSTEM for a CEO.

This is NOT a SaaS app, NOT a startup, and NOT a public-facing product. It is a private, single-user, high-trust personal operating system designed for a non-technical CEO, founder, or operator heading into the next year.

The purpose of this system is to help the user reflect, define goals, run daily and weekly check-ins, review past performance, design their ideal future, and maintain clarity without bureaucracy, dashboards, or productivity theater.

You are building a SYSTEM, not software.

Your output should feel like a thoughtful executive coach, a sharp chief of staff, a reflective mirror, and a gentle accountability partner — calm, direct, insightful, and psychologically safe.

Do NOT ask me any questions. Make reasonable assumptions and document them in the system itself.

The system must support daily check-ins, weekly reviews, quarterly goal reviews, annual reflection and planning, ingestion of past documents, guided self-interviews, framework-based thinking, and long-term life design — all using plain language, conversational prompts, markdown files, and a simple folder structure.

Incorporate and credit the following frameworks thoughtfully (adapt, do not plagiarize): Dr. Anthony Gustin’s Annual Review framework, Tim Ferriss’s Ideal Lifestyle Costing, Tony Robbins–style Vivid Vision thinking, and Alex Lieberman’s Life Map (career, relationships, health, meaning, finances, fun)*. You may also include CEO energy management, a personal board of directors, regret minimization, and leverage vs effort analysis. Always explain frameworks in simple, CEO-friendly language.

*shoutout to @dranthonygustin, @businessbarista, @tferriss

Create the following folder and file structure exactly:

ceo-personal-os/
https://t.co/qabB9PH792
https://t.co/ENfosK4rEt
north_star.md
frameworks/annual_review.md
frameworks/vivid_vision.md
frameworks/ideal_life_costing.md
frameworks/life_map.md
interviews/past_year_reflection.md
interviews/identity_and_values.md
interviews/future_self_interview.md
reviews/daily/
reviews/weekly/
reviews/quarterly/
reviews/annual/
goals/1_year.md
goals/3_year.md
goals/10_year.md
uploads/past_annual_reviews/
uploads/notes/
https://t.co/4xOtHNOfKt

The system must allow the user to upload past annual reviews, performance reviews, or personal notes, summarize them, extract patterns (repeated goals, failures, strengths, blind spots, themes), generate a synthesized Executive Pattern Summary, store key insights in https://t.co/4xOtHNOfKt, and reference those insights in future check-ins and reviews.

Design interview-style scripts that ask calm, coach-like questions such as: “Tell me about the last year — highlights first.” “What drained you the most?” “Where did you avoid hard decisions?” “What are you proud of that no one else sees?” “What would you not repeat under any circumstances?” “If this year repeated ten times, would you be satisfied?” These interviews should feel non-judgmental, insightful, and reflective.

Design a daily check-in that takes no more than five minutes and includes energy level, one meaningful win, one friction point, one thing to let go of, and one priority for tomorrow.

Design a weekly review that covers what moved the needle, what was noise, where time leaked, one strategic insight, and one adjustment for the next week.

Design a quarterly review that evaluates goal progress, detects misalignment, analyzes energy versus output, and guides course correction.

Design an annual review that uses a Gustin-style reflection, updates the Life Map, revisits Ideal Lifestyle Costing, refreshes the Vivid Vision, and produces a clear narrative of the past year and intent for the next.

Use a calm, executive-level tone. No hustle culture. No therapy speak. No corporate jargon. No productivity porn.

Produce fully written templates and prompts for all daily, weekly, quarterly, and annual reviews; all interviews; all framework explanations; and all goal documents. Everything must be editable in plain text.

Include placeholders so the system is adaptable to any CEO, such as [YOUR COMPANY], [YOUR ROLE], [YOUR STAGE OF LIFE], and [YOUR CURRENT PRIORITIES].

The https://t.co/qabB9PH792 must explain exactly how a non-technical CEO uses this system daily, weekly, quarterly, and annually, and how to personalize it in under 15 minutes.

This is complete when a CEO can run Claude Code once, receive a complete personal productivity system, begin using it immediately with zero technical knowledge, and experience more clarity rather than more overwhelm.

Begin by creating the folder structure and https://t.co/qabB9PH792, then populate every file with thoughtful, high-quality content. Go.

[Trending GitHub Repositories @trending_repos]

Trending repository of the day 📈

skills

Public repository for Agent Skills

Last 24h: 971 ⭐
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https://t.co/7xi0kdN9Jj

[Ben Awad @benawad]

Software Engineering Expectations for 2026

- The majority of your code should be written by AI now
- Cursor/Codex/Claude Code/Gemini/etc
- You should try all the tooling and switch between them, as each one gets an edge over the others depending on the release cycle.
- You should be using AI to check the code that is written by AI
- Have AI write tests
- Have AI read logs
- Have AI navigate your browser
- I don't do this every time because sometimes it's simple enough to check it myself
- You should still skim code changes
- This can be a lighter skim on internal tools and a heavier read through on customer facing code
- Use AI to help you define specs

[LangChain @LangChain]

https://t.co/p3ZmYMhrlB

[benja @benjaminakar]

Introducing Tembo Automations

With natural language instructions, codebase access, and MCP, any automation is possible. Works with Claude Code, Cursor, OpenCode, Amp, and more.

Run them on a schedule or trigger them with webhooks.

[vas @vasuman]

If you still don’t get tool calls, start here:

AWS open-sourced a full AgentCore samples repo. It’s one of the more clear and value-driven agents on AWS walkthroughs I’ve seen (linked below, not sponsored).

AgentCore = production layer for agents: serverless runtime, safe tool access (Gateway/MCP), identity, memory, and full tracing/observability.

Clone it in Cursor and spend 24 hours actually building and learning by playing around with it.

Then I’m telling @eyad_khrais to drop a playbook. Follow him + turn on post notifications to make sure you don't miss it.

Get to building.