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Job searching is fundamentally broken. "Spray and pray" is officially dead. 🛑

Right now, candidates are keeping dozens of browser tabs open, copy-pasting one generic CV everywhere, while spam bots fire off 800 applications a day and clog hiring inboxes.

Hiring managers can spot an untailored resume within three seconds. The result? Most qualified applicants get zero replies and never understand why.

Quality applications beat spam bots every single time.

A new open-source repository with 19,500+ stars (hitting #1 on GitHub) turns Claude into your personal job search agent:

📄 Reads the posting: Extracts key requirements directly from the job posting URL.

✏️ Tailors on autopilot: Rewrites your CV and drafts a custom cover letter specifically for that exact role.

🧠 Drafter-Reviewer Architecture: Uses two distinct agents, one writes the draft, and the second reads it back to push back on weak phrasing until it's sharp.

📊 Built-in prep: Generates interview prep materials and tracks your application status automatically.

Stop applying to 100 jobs generically. Apply to fewer jobs, far more seriously.

Swipe through the carousel to see the exact workflow teardown. 👉

Want the repo link? Comment "JOBS" below, and I’ll DM you the free, open-source setup instantly! 📥

Designing the software gets you into the market.

Controlling the supply chain lets you dictate it. 🔬

Don't let surface-level hype hide where the real power lies. While the entire tech industry races to build cutting-edge AI models, the breakdown of advanced node (sub-5nm) capacity share for 2026 reveals the true bottleneck: TSMC controls 65%, Samsung holds 20%, and Intel Foundry sits at 10%. With just three foundries holding over 95% of sub-5nm manufacturing capacity, the biggest winners aren't just designing the future, they own the essential infrastructure everyone else depends on.

The formula is simple:

1️⃣ Identify.
2️⃣ Secure.
3️⃣ Control.
4️⃣ Dominate.

You don't just win by riding the wave of an industry boom, but by positioning yourself at the critical bottlenecks that power it. Keep building leverage. 🚀

Notion's Darkroom just dropped an episode on "How the Best Product Teams Are Actually Using AI in Their Workflow."
I've been thinking about the specifics ever since.

We work with product teams every week. The ones using AI most effectively aren't using it for everything, they've made very deliberate choices about exactly where it sits in the workflow. Discovery stays human. Synthesis gets AI assist. Final decisions always have a name attached. The discipline is the point.

What this episode captures that most AI tool content misses: workflow integration is a design problem, not a software problem. You can give a team every AI tool available and watch them use it randomly, ineffectively, and inconsistently. The teams winning have designed the integration intentionally, who uses what, when, and what they do with the output.

We spent 3 weeks mapping our own workflow before touching any AI tools. Every step. Every decision point. Every handoff. Then we asked: where does AI accelerate this, and where does it introduce noise? The map saved us from buying tools we didn't need and helped us see where we actually had leverage.

AI tool adoption without workflow design is just expensive chaos.

Has your team designed where AI sits in your workflow, or just started using it everywhere?
Check the link here: https://www.youtube.com/
Notion, Darkroom Podcast

Notion To buy the handle please contact: [email protected]

AI agents can write complex code, call APIs, and build full-stack apps, but ask them to edit a Word document or Excel spreadsheet, and they hit a wall. 🛑

The most common business format in the world was completely invisible to every AI agent. Every analyst, assistant, and auditor workflow was blocked at the Microsoft Office layer.

That block just disappeared.

A new open-source tool called OfficeCLI (19,000+ stars in weeks) gives AI agents full control over .docx, .xlsx, and .pptx files, no Microsoft subscription or cloud dependencies required.

This unlocks real business work across every department:

📊 Financial Analysts: Auto-generate Q4 forecasts in Excel with 350+ live, working formulas.

🤝 Sales Teams: Build 20-slide PowerPoint pitch decks with auto-populated speaker notes.

⚖️ Legal Teams: Draft and format 40-page Word contracts automatically.

📣 Content Teams: Automate complex reports, briefs, and client proposals in seconds.

Manual office work is being replaced by $0 local AI ex*****on.

Swipe through to see how it works under the hood. 👉

Want to give your AI agents full Office control today? Comment "OFFICE" below, and I’ll DM you the free, MIT-licensed repo link instantly! 📥

Starting small keeps you grounded.

Scaling exponentially redefines the industry. 📈

Don't let modest beginnings fool you into underestimating your true trajectory. Looking at Nvidia's Data Center Revenue curve, climbing from an "Early State" of $15 billion in FY2023 to an "Unprecedented Surge" of $115 billion in FY2025, the market shows that when long-term technical conviction aligns with a massive global shift, growth isn't just steady, it becomes unstoppable.

The formula is simple:

1️⃣ Position.
2️⃣ Persevere.
3️⃣ Scale.
4️⃣ Dominate.

You don't need a massive starting point to create an empire, but you do need to align yourself with where the future is headed. Keep building momentum. 🚀

Freakonomics Radio just put out an episode on "What the Data Actually Says About AI Productivity, vs. What Everyone Believes."
I've been sitting with the gap between those two things ever since.

We hear the productivity narrative constantly. AI is transforming output. Teams are 10x faster. Everything is better. And some of it is true. But the aggregate data is messier than the anecdotes. Productivity at the macro level hasn't moved the way the tool adoption numbers would predict.

What Stephen Dubner pulls apart so carefully, and this is the episode worth sending to your skeptical board member, is the difference between measuring activity and measuring outcomes. AI is clearly increasing activity. Whether it's increasing the right outcomes is a much harder question that most companies aren't even asking.

We added one metric to every AI-assisted project we run: outcome delta, not output delta. Not "how much faster did we produce this" but "did the thing we produced actually perform better?" The gap between those two numbers has been humbling and clarifying.

AI is making us more productive at producing things. Whether those things are more valuable is a different question entirely.

How does your team measure the quality of AI-assisted output, not just the quantity?
Check the link here: https://www.youtube.com/
Stephen Dubner, Freakonomics Radio

Freakonomics Radio Network Shows that explore the hidden side of everything with Stephen Dubner. Freakonomics Radio is about how things work. New episodes on Fridays. Better in Person is about how people work. New episodes on Tuesdays. Explore more: Freakonomics Radio → https://freakonomics.com/series/freakonomics-radio No ...

The most used AI agent in the world isn't owned by Big Tech. 🛑

We are constantly told that the "frontier" labs, OpenAI, Anthropic, and Google,will always win. The assumption is that building agent tools requires the biggest compute budget, leaving small research teams unable to compete.

But the numbers tell a completely different story.

A free, open-source repository just quietly became the #1 AI agent in the world, beating out ChatGPT, Claude, and Gemini.

Built by a small lab called Nous Research, it reached 218,000 stars with zero hype, no ad budget, and no PR team.

It crossed 140k stars in under 3 months.

NVIDIA confirmed via OpenRouter that it is the most-used AI agent globally.

Here is why this shifts the entire industry: Frontier models still matter, but this repo replaces the agent layer around the model, not the model itself. Hermes runs on top of whatever model you choose, whether that is GPT, Claude, Gemini, or a local LLM.

The moat isn't the LLM anymore; it's what wraps it. And if the wrapper is free and open, the big tech moat gets a lot smaller.

Swipe through to see why the agent layer is becoming open infrastructure. 👉

Want to try the most-used agent in the world for free? Comment "HERMES" below, and I’ll DM you the repo link!

Having one winning product secures your present.

Building a diverse ecosystem secures your empire. 🌐

Don't let the success of your primary cash cow blind you to the power of diversification. As shown in the chart of Google's Q2 2026 Income Statement, their massive $119.8B total revenue isn't just from one source. While Search remains the bedrock at $63.3B, they are actively stacking multiple massive growth engines, from Subscriptions, Platforms & Devices at $12.9B and YouTube Ads at $11.1B, to the absolute hyper-growth of Google Cloud exploding by 82% year-over-year to hit $24.8B. True market leaders don't just rest on a single pillar; they build an interconnected ecosystem where every segment compounds their overall dominance.

The formula is simple:

1️⃣ Establish.
2️⃣ Diversify.
3️⃣ Accelerate.
4️⃣ Scale.

You don't build a sustainable powerhouse with one hit offering, but by creating a portfolio of relentless growth engines. Keep expanding your ecosystem. 🚀

Odd Lots just released an episode on "The Economics of the AI Boom, Who Is Actually Paying for All of This?"
I haven't been able to stop thinking about the numbers.

The capital being deployed into AI infrastructure right now is historically unprecedented. And the revenue being generated against it is a fraction of what would justify the investment under any normal model. We've seen this shape before. The question isn't whether the boom is real. It's who survives when the math gets rebalanced.

What Joe and Tracy do better than anyone is follow the money without the hype. And what this episode makes clear is that the winners in AI won't necessarily be the companies building the most impressive technology. They'll be the companies that figured out the unit economics before the funding cycle turned.

We built a simple internal test for every AI initiative we evaluate: what does it cost per decision, what's the decision worth, and when does the math work at scale? We kill projects that can't answer that in one page. The constraint has saved us from a lot of expensive enthusiasm.

The AI wave is real. The economics of who profits from it are still being written.

What's the unit economic model behind the AI investment your company is making right now?
Check the link here: https://www.youtube.com/
Joe Weisenthal & Tracy Alloway, Odd Lots, Bloomberg

Bloomberg Podcasts Bloomberg Podcasts brings you the best video and audio programming from Bloomberg. Watch for news and analysis on the stories making headlines in business, technology and financial markets.

Paid tools hide the real cost behind convenience. 🛑

Every provider wants you locked into their billing. You pay per seat, per token, per provider, and you never compare. But one free repo just replaced $200/mo in AI subscriptions. The team didn't buy more API credits; they routed around the bill entirely, and it still worked.

Instead of managing separate bills, they used OmniRoute—a free gateway routing across 237+ providers.

Here is how this architecture wins:

Auto-fallback switches providers when one hits a limit.

Compression shrinks requests before they hit the model.

This multi-tool setup plugs directly into Claude Code, Cursor, Codex, and Copilot.

It runs locally with one command and zero cloud dependency.

A $0/mo stack with 50 free providers beats a $200/mo stack with one. Route smart. Pay nothing. Ship anyway.

Swipe through the carousel to see how this local AI gateway works. 👉

Want the repo link? Comment "OMNI" below, and I’ll DM it to you for free so you can set it up before your next AI bill hits! 📥