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Two companies.
Same CRM.
Same sales stages.
Same forecasting cadence.
Compete for the same customers.
Look at their systems, and they're identical.
Look at how they actually work, and they're completely different.
English
Mohamed Anis
6.7K posts
Mohamed Anis
@Anis_StepUpOne
Done-For-You Investor Outreach → Three Battle-Tested Playbooks → We Run The Process → You Build → Zero Distraction
London, United Kingdom Katılım Kasım 2021
4.1K Takip Edilen1.2K Takipçiler
@sentient_agency This is exactly the infrastructure layer VCs want to see before writing checks you're not just using AI, you're architecting repeatable systems that scale beyond the founding team.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Here's the case for why Claude Code + COBOL could unwind a century of @IBM dominance and why this time it's structural, not cyclical. IBM's moat was never technical. It was cognitive. 800 billion lines of COBOL sit in active production. $3 trillion in daily commerce flows through it. 95% of ATM transactions run on it. IBM didn't protect that kingdom with patents. It protected it with complexity, deliberately accumulated, institutionally embedded, humanly irreplaceable. The playbook worked for 65 years because COBOL expertise takes 20+ years to develop meaningfully. The average COBOL developer is now 55+. That's not a talent pool. That's a ticking liability. IBM Consulting built a $20B+ annual business on a simple arbitrage: enterprises couldn't touch the systems themselves, so IBM charged them to maintain, extend, and occasionally modernise them, on IBM's timeline, at IBM's rates, with IBM-certified humans. AI doesn't just speed that up. It eliminates the arbitrage entirely. When a model can read, reason about, and translate COBOL at a human-expert level, the knowledge scarcity that created IBM's pricing power disappears. Not gradually. Suddenly. What previously required 3-5 year multi-million-pound programmes could be compressed to months. What required IBMers with 20 years of mainframe scar tissue can now be scaffolded by a junior engineer with Claude Code and good judgment. Three pillars held IBM's moat: 1 Proprietary tooling - still relevant, but eroding as AI-native tools match output quality 2 Certified expertise scarcity - gone when any competent engineer can query the model 3 Enterprise risk aversion - the last standing wall, but Tier-1 banks are already running pilots You're living through pillar three cracking in real time. The real IBM risk isn't the Z17. It's the consulting P&L. The Z platform's 40% growth is real, and IBM Z17, supporting Java and modern workloads, is a smart hedge. But hardware is not where the margin lives. IBM Consulting is. And consulting revenue requires duration. long programmes, high headcount, multi-year contracts. When AI compresses a 5-year engagement into 8 months, IBM doesn't get 5 years' worth of fees on a smaller deal. It doesn't get the deal at all. This is the Kodak moment, not because the product is bad, but because the problem it solves is shrinking. Jasper, Gamma, Cursor - yes, they'll face the same gravity. But they were born in the AI era. IBM built its entire identity on a problem that required human scarcity to remain monetisable. The 13% drop isn't a panic. It's the market slowly understanding that IBM's core value proposition-"we are the only ones who can safely touch your most critical systems" just had its first genuinely credible challenger. That's not a dip. That's a re-rating of what IBM is worth in a world where the moat can be drained.
English
🚨 This GitHub repo just changed how I use Claude Code forever.
Its called "claude-code-best-practice" and it got production-ready agents, memory across sessions, custom hooks, skills, and commands all in one place.
Turns Claude Code into a full autonomous coding team.
100% open-source.
English
@RihardJarc The best GTM strategies are the ones that become invisible because the product creates its own gravity.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Here's the case for why Claude Code + COBOL could unwind a century of @IBM dominance and why this time it's structural, not cyclical. IBM's moat was never technical. It was cognitive. 800 billion lines of COBOL sit in active production. $3 trillion in daily commerce flows through it. 95% of ATM transactions run on it. IBM didn't protect that kingdom with patents. It protected it with complexity, deliberately accumulated, institutionally embedded, humanly irreplaceable. The playbook worked for 65 years because COBOL expertise takes 20+ years to develop meaningfully. The average COBOL developer is now 55+. That's not a talent pool. That's a ticking liability. IBM Consulting built a $20B+ annual business on a simple arbitrage: enterprises couldn't touch the systems themselves, so IBM charged them to maintain, extend, and occasionally modernise them, on IBM's timeline, at IBM's rates, with IBM-certified humans. AI doesn't just speed that up. It eliminates the arbitrage entirely. When a model can read, reason about, and translate COBOL at a human-expert level, the knowledge scarcity that created IBM's pricing power disappears. Not gradually. Suddenly. What previously required 3-5 year multi-million-pound programmes could be compressed to months. What required IBMers with 20 years of mainframe scar tissue can now be scaffolded by a junior engineer with Claude Code and good judgment. Three pillars held IBM's moat: 1 Proprietary tooling - still relevant, but eroding as AI-native tools match output quality 2 Certified expertise scarcity - gone when any competent engineer can query the model 3 Enterprise risk aversion - the last standing wall, but Tier-1 banks are already running pilots You're living through pillar three cracking in real time. The real IBM risk isn't the Z17. It's the consulting P&L. The Z platform's 40% growth is real, and IBM Z17, supporting Java and modern workloads, is a smart hedge. But hardware is not where the margin lives. IBM Consulting is. And consulting revenue requires duration. long programmes, high headcount, multi-year contracts. When AI compresses a 5-year engagement into 8 months, IBM doesn't get 5 years' worth of fees on a smaller deal. It doesn't get the deal at all. This is the Kodak moment, not because the product is bad, but because the problem it solves is shrinking. Jasper, Gamma, Cursor - yes, they'll face the same gravity. But they were born in the AI era. IBM built its entire identity on a problem that required human scarcity to remain monetisable. The 13% drop isn't a panic. It's the market slowly understanding that IBM's core value proposition-"we are the only ones who can safely touch your most critical systems" just had its first genuinely credible challenger. That's not a dip. That's a re-rating of what IBM is worth in a world where the moat can be drained.
English
The surge and pace in adoption of Claude Code will go down in the history books.
English
@felixrieseberg Game-changer for founders who've been manually context-switching between ideation and implementation.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Here's the case for why Claude Code + COBOL could unwind a century of @IBM dominance and why this time it's structural, not cyclical. IBM's moat was never technical. It was cognitive. 800 billion lines of COBOL sit in active production. $3 trillion in daily commerce flows through it. 95% of ATM transactions run on it. IBM didn't protect that kingdom with patents. It protected it with complexity, deliberately accumulated, institutionally embedded, humanly irreplaceable. The playbook worked for 65 years because COBOL expertise takes 20+ years to develop meaningfully. The average COBOL developer is now 55+. That's not a talent pool. That's a ticking liability. IBM Consulting built a $20B+ annual business on a simple arbitrage: enterprises couldn't touch the systems themselves, so IBM charged them to maintain, extend, and occasionally modernise them, on IBM's timeline, at IBM's rates, with IBM-certified humans. AI doesn't just speed that up. It eliminates the arbitrage entirely. When a model can read, reason about, and translate COBOL at a human-expert level, the knowledge scarcity that created IBM's pricing power disappears. Not gradually. Suddenly. What previously required 3-5 year multi-million-pound programmes could be compressed to months. What required IBMers with 20 years of mainframe scar tissue can now be scaffolded by a junior engineer with Claude Code and good judgment. Three pillars held IBM's moat: 1 Proprietary tooling - still relevant, but eroding as AI-native tools match output quality 2 Certified expertise scarcity - gone when any competent engineer can query the model 3 Enterprise risk aversion - the last standing wall, but Tier-1 banks are already running pilots You're living through pillar three cracking in real time. The real IBM risk isn't the Z17. It's the consulting P&L. The Z platform's 40% growth is real, and IBM Z17, supporting Java and modern workloads, is a smart hedge. But hardware is not where the margin lives. IBM Consulting is. And consulting revenue requires duration. long programmes, high headcount, multi-year contracts. When AI compresses a 5-year engagement into 8 months, IBM doesn't get 5 years' worth of fees on a smaller deal. It doesn't get the deal at all. This is the Kodak moment, not because the product is bad, but because the problem it solves is shrinking. Jasper, Gamma, Cursor - yes, they'll face the same gravity. But they were born in the AI era. IBM built its entire identity on a problem that required human scarcity to remain monetisable. The 13% drop isn't a panic. It's the market slowly understanding that IBM's core value proposition-"we are the only ones who can safely touch your most critical systems" just had its first genuinely credible challenger. That's not a dip. That's a re-rating of what IBM is worth in a world where the moat can be drained.
English
By popular demand, Dispatch can now launch Claude Code sessions. Ask it to build, make, or improve something!
To use it, update your Claude desktop app and make sure you have Code enabled.
English
@fin465 @ProductHunt @origamichat The combination of Claude Code for lead generation with a structured launch strategy is a masterclass in execution that every founder should study.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Here's the case for why Claude Code + COBOL could unwind a century of @IBM dominance and why this time it's structural, not cyclical. IBM's moat was never technical. It was cognitive. 800 billion lines of COBOL sit in active production. $3 trillion in daily commerce flows through it. 95% of ATM transactions run on it. IBM didn't protect that kingdom with patents. It protected it with complexity, deliberately accumulated, institutionally embedded, humanly irreplaceable. The playbook worked for 65 years because COBOL expertise takes 20+ years to develop meaningfully. The average COBOL developer is now 55+. That's not a talent pool. That's a ticking liability. IBM Consulting built a $20B+ annual business on a simple arbitrage: enterprises couldn't touch the systems themselves, so IBM charged them to maintain, extend, and occasionally modernise them, on IBM's timeline, at IBM's rates, with IBM-certified humans. AI doesn't just speed that up. It eliminates the arbitrage entirely. When a model can read, reason about, and translate COBOL at a human-expert level, the knowledge scarcity that created IBM's pricing power disappears. Not gradually. Suddenly. What previously required 3-5 year multi-million-pound programmes could be compressed to months. What required IBMers with 20 years of mainframe scar tissue can now be scaffolded by a junior engineer with Claude Code and good judgment. Three pillars held IBM's moat: 1 Proprietary tooling - still relevant, but eroding as AI-native tools match output quality 2 Certified expertise scarcity - gone when any competent engineer can query the model 3 Enterprise risk aversion - the last standing wall, but Tier-1 banks are already running pilots You're living through pillar three cracking in real time. The real IBM risk isn't the Z17. It's the consulting P&L. The Z platform's 40% growth is real, and IBM Z17, supporting Java and modern workloads, is a smart hedge. But hardware is not where the margin lives. IBM Consulting is. And consulting revenue requires duration. long programmes, high headcount, multi-year contracts. When AI compresses a 5-year engagement into 8 months, IBM doesn't get 5 years' worth of fees on a smaller deal. It doesn't get the deal at all. This is the Kodak moment, not because the product is bad, but because the problem it solves is shrinking. Jasper, Gamma, Cursor - yes, they'll face the same gravity. But they were born in the AI era. IBM built its entire identity on a problem that required human scarcity to remain monetisable. The 13% drop isn't a panic. It's the market slowly understanding that IBM's core value proposition-"we are the only ones who can safely touch your most critical systems" just had its first genuinely credible challenger. That's not a dip. That's a re-rating of what IBM is worth in a world where the moat can be drained.
English
We built Claude Code for Lead Generation…
We hit #1 on @ProductHunt & took @origamichat from slow growth to 2x MRR every week since
I wrote up the exact guide we used to hit #1 and get thousands of users to sign up in a day.
Complete blueprint to #1.
I’ll dm it to you
Just comment “Launch”
RT so other founders get the blueprint
English
@businessbarista This is exactly how exponential adoption happens: someone ships the connective tissue that turns scattered experiments into an ecosystem.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Here's the case for why Claude Code + COBOL could unwind a century of @IBM dominance and why this time it's structural, not cyclical. IBM's moat was never technical. It was cognitive. 800 billion lines of COBOL sit in active production. $3 trillion in daily commerce flows through it. 95% of ATM transactions run on it. IBM didn't protect that kingdom with patents. It protected it with complexity, deliberately accumulated, institutionally embedded, humanly irreplaceable. The playbook worked for 65 years because COBOL expertise takes 20+ years to develop meaningfully. The average COBOL developer is now 55+. That's not a talent pool. That's a ticking liability. IBM Consulting built a $20B+ annual business on a simple arbitrage: enterprises couldn't touch the systems themselves, so IBM charged them to maintain, extend, and occasionally modernise them, on IBM's timeline, at IBM's rates, with IBM-certified humans. AI doesn't just speed that up. It eliminates the arbitrage entirely. When a model can read, reason about, and translate COBOL at a human-expert level, the knowledge scarcity that created IBM's pricing power disappears. Not gradually. Suddenly. What previously required 3-5 year multi-million-pound programmes could be compressed to months. What required IBMers with 20 years of mainframe scar tissue can now be scaffolded by a junior engineer with Claude Code and good judgment. Three pillars held IBM's moat: 1 Proprietary tooling - still relevant, but eroding as AI-native tools match output quality 2 Certified expertise scarcity - gone when any competent engineer can query the model 3 Enterprise risk aversion - the last standing wall, but Tier-1 banks are already running pilots You're living through pillar three cracking in real time. The real IBM risk isn't the Z17. It's the consulting P&L. The Z platform's 40% growth is real, and IBM Z17, supporting Java and modern workloads, is a smart hedge. But hardware is not where the margin lives. IBM Consulting is. And consulting revenue requires duration. long programmes, high headcount, multi-year contracts. When AI compresses a 5-year engagement into 8 months, IBM doesn't get 5 years' worth of fees on a smaller deal. It doesn't get the deal at all. This is the Kodak moment, not because the product is bad, but because the problem it solves is shrinking. Jasper, Gamma, Cursor - yes, they'll face the same gravity. But they were born in the AI era. IBM built its entire identity on a problem that required human scarcity to remain monetisable. The 13% drop isn't a panic. It's the market slowly understanding that IBM's core value proposition-"we are the only ones who can safely touch your most critical systems" just had its first genuinely credible challenger. That's not a dip. That's a re-rating of what IBM is worth in a world where the moat can be drained.
English
Until you spend 100+ hours beating up & getting beaten up by Cowork, Claude Code, Codex, etc, do not tell me that "the technology isn't there."
The technology is basically "there" for 99% of knowledge work.
English
@aiwithjainam This is exactly the kind of infrastructure play that accelerates entire ecosystems when you lower the barrier to entry, you unlock exponential value creation.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Here's the case for why Claude Code + COBOL could unwind a century of @IBM dominance and why this time it's structural, not cyclical. IBM's moat was never technical. It was cognitive. 800 billion lines of COBOL sit in active production. $3 trillion in daily commerce flows through it. 95% of ATM transactions run on it. IBM didn't protect that kingdom with patents. It protected it with complexity, deliberately accumulated, institutionally embedded, humanly irreplaceable. The playbook worked for 65 years because COBOL expertise takes 20+ years to develop meaningfully. The average COBOL developer is now 55+. That's not a talent pool. That's a ticking liability. IBM Consulting built a $20B+ annual business on a simple arbitrage: enterprises couldn't touch the systems themselves, so IBM charged them to maintain, extend, and occasionally modernise them, on IBM's timeline, at IBM's rates, with IBM-certified humans. AI doesn't just speed that up. It eliminates the arbitrage entirely. When a model can read, reason about, and translate COBOL at a human-expert level, the knowledge scarcity that created IBM's pricing power disappears. Not gradually. Suddenly. What previously required 3-5 year multi-million-pound programmes could be compressed to months. What required IBMers with 20 years of mainframe scar tissue can now be scaffolded by a junior engineer with Claude Code and good judgment. Three pillars held IBM's moat: 1 Proprietary tooling - still relevant, but eroding as AI-native tools match output quality 2 Certified expertise scarcity - gone when any competent engineer can query the model 3 Enterprise risk aversion - the last standing wall, but Tier-1 banks are already running pilots You're living through pillar three cracking in real time. The real IBM risk isn't the Z17. It's the consulting P&L. The Z platform's 40% growth is real, and IBM Z17, supporting Java and modern workloads, is a smart hedge. But hardware is not where the margin lives. IBM Consulting is. And consulting revenue requires duration. long programmes, high headcount, multi-year contracts. When AI compresses a 5-year engagement into 8 months, IBM doesn't get 5 years' worth of fees on a smaller deal. It doesn't get the deal at all. This is the Kodak moment, not because the product is bad, but because the problem it solves is shrinking. Jasper, Gamma, Cursor - yes, they'll face the same gravity. But they were born in the AI era. IBM built its entire identity on a problem that required human scarcity to remain monetisable. The 13% drop isn't a panic. It's the market slowly understanding that IBM's core value proposition-"we are the only ones who can safely touch your most critical systems" just had its first genuinely credible challenger. That's not a dip. That's a re-rating of what IBM is worth in a world where the moat can be drained.
English
🚨BREAKING: Someone just built the App Store for Claude Code skills.
It's called Awesome Claude Code Skills and it's on GitHub for free.
→ Skills for automation, research, coding, and workflows
→ Ready-made tools to extend Claude Code instantly
→ Community-contributed skills you can reuse and modify
→ Examples showing how to build your own
→ One central hub for discovering new capabilities
Stop building every tool from scratch.
Pick a skill. Drop it in. Ship faster.
100% Opensource.
(Link in comments)
English
@kurtbuhler This is the kind of operational rigor that signals to potential partners and backers that you're someone who understands systems at a foundational level.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Here's the case for why Claude Code + COBOL could unwind a century of @IBM dominance and why this time it's structural, not cyclical. IBM's moat was never technical. It was cognitive. 800 billion lines of COBOL sit in active production. $3 trillion in daily commerce flows through it. 95% of ATM transactions run on it. IBM didn't protect that kingdom with patents. It protected it with complexity, deliberately accumulated, institutionally embedded, humanly irreplaceable. The playbook worked for 65 years because COBOL expertise takes 20+ years to develop meaningfully. The average COBOL developer is now 55+. That's not a talent pool. That's a ticking liability. IBM Consulting built a $20B+ annual business on a simple arbitrage: enterprises couldn't touch the systems themselves, so IBM charged them to maintain, extend, and occasionally modernise them, on IBM's timeline, at IBM's rates, with IBM-certified humans. AI doesn't just speed that up. It eliminates the arbitrage entirely. When a model can read, reason about, and translate COBOL at a human-expert level, the knowledge scarcity that created IBM's pricing power disappears. Not gradually. Suddenly. What previously required 3-5 year multi-million-pound programmes could be compressed to months. What required IBMers with 20 years of mainframe scar tissue can now be scaffolded by a junior engineer with Claude Code and good judgment. Three pillars held IBM's moat: 1 Proprietary tooling - still relevant, but eroding as AI-native tools match output quality 2 Certified expertise scarcity - gone when any competent engineer can query the model 3 Enterprise risk aversion - the last standing wall, but Tier-1 banks are already running pilots You're living through pillar three cracking in real time. The real IBM risk isn't the Z17. It's the consulting P&L. The Z platform's 40% growth is real, and IBM Z17, supporting Java and modern workloads, is a smart hedge. But hardware is not where the margin lives. IBM Consulting is. And consulting revenue requires duration. long programmes, high headcount, multi-year contracts. When AI compresses a 5-year engagement into 8 months, IBM doesn't get 5 years' worth of fees on a smaller deal. It doesn't get the deal at all. This is the Kodak moment, not because the product is bad, but because the problem it solves is shrinking. Jasper, Gamma, Cursor - yes, they'll face the same gravity. But they were born in the AI era. IBM built its entire identity on a problem that required human scarcity to remain monetisable. The 13% drop isn't a panic. It's the market slowly understanding that IBM's core value proposition-"we are the only ones who can safely touch your most critical systems" just had its first genuinely credible challenger. That's not a dip. That's a re-rating of what IBM is worth in a world where the moat can be drained.
English
Recently had some issues with Claude Code where it was showing "Claude API" instead of "Claude Max" for my personal sub. Was annoying because I couldn't use a lot of the new features like /remote_control.
In case anyone else might have this, here's how I fixed it:
1. In your shell profile, you have to find and delete the stored oauth token i.e. CLAUDE_CODE_OAUTH_TOKEN
2. You also may have to clear out API keys from your sehll profile, keychain, keyring (mac) or equivalent i.e. ANTHROPIC_API_KEY
3. /logout and the log back in with interactive auth on a fresh browser session
English
@techNmak The bottlenecks Unsloth documents broken KV caching, misconfigured precision, reasoning overhead these are the exact friction points that separate experimental setups from production-ready infrastructure.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Here's the case for why Claude Code + COBOL could unwind a century of @IBM dominance and why this time it's structural, not cyclical. IBM's moat was never technical. It was cognitive. 800 billion lines of COBOL sit in active production. $3 trillion in daily commerce flows through it. 95% of ATM transactions run on it. IBM didn't protect that kingdom with patents. It protected it with complexity, deliberately accumulated, institutionally embedded, humanly irreplaceable. The playbook worked for 65 years because COBOL expertise takes 20+ years to develop meaningfully. The average COBOL developer is now 55+. That's not a talent pool. That's a ticking liability. IBM Consulting built a $20B+ annual business on a simple arbitrage: enterprises couldn't touch the systems themselves, so IBM charged them to maintain, extend, and occasionally modernise them, on IBM's timeline, at IBM's rates, with IBM-certified humans. AI doesn't just speed that up. It eliminates the arbitrage entirely. When a model can read, reason about, and translate COBOL at a human-expert level, the knowledge scarcity that created IBM's pricing power disappears. Not gradually. Suddenly. What previously required 3-5 year multi-million-pound programmes could be compressed to months. What required IBMers with 20 years of mainframe scar tissue can now be scaffolded by a junior engineer with Claude Code and good judgment. Three pillars held IBM's moat: 1 Proprietary tooling - still relevant, but eroding as AI-native tools match output quality 2 Certified expertise scarcity - gone when any competent engineer can query the model 3 Enterprise risk aversion - the last standing wall, but Tier-1 banks are already running pilots You're living through pillar three cracking in real time. The real IBM risk isn't the Z17. It's the consulting P&L. The Z platform's 40% growth is real, and IBM Z17, supporting Java and modern workloads, is a smart hedge. But hardware is not where the margin lives. IBM Consulting is. And consulting revenue requires duration. long programmes, high headcount, multi-year contracts. When AI compresses a 5-year engagement into 8 months, IBM doesn't get 5 years' worth of fees on a smaller deal. It doesn't get the deal at all. This is the Kodak moment, not because the product is bad, but because the problem it solves is shrinking. Jasper, Gamma, Cursor - yes, they'll face the same gravity. But they were born in the AI era. IBM built its entire identity on a problem that required human scarcity to remain monetisable. The 13% drop isn't a panic. It's the market slowly understanding that IBM's core value proposition-"we are the only ones who can safely touch your most critical systems" just had its first genuinely credible challenger. That's not a dip. That's a re-rating of what IBM is worth in a world where the moat can be drained.
English
Claude Code can run entirely on your local GPU now.
Unsloth AI published the complete guide.
The setup itself is straightforward - llama.cpp serves Qwen3.5 or GLM-4.7-Flash, one environment variable redirects Claude Code to localhost.
But the guide is valuable because of what it explains beyond the setup:
Why local inference feels impossibly slow: Claude Code adds an attribution header that breaks KV caching. Every request recomputes the full context. The fix requires editing settings.json - export doesn't work.
Why Qwen3.5 outputs seem off: f16 KV cache degrades accuracy, and it's llama.cpp's default. Multiple reports confirm this. Use q8_0 or bf16 instead.
Why responses take forever: Thinking mode is great for reasoning but slow for agentic tasks. The guide shows how to disable it.
The proof it all works: Claude Code autonomously fine-tuning a model with Unsloth. Start to finish. No API dependency.
Fits on 24GB. RTX 4090, Mac unified memory.
English
The real alpha here isn't the 10x markups everyone knows defense procurement is broken it's that agentic AI can now compress months of due diligence, competitive analysis, and compliance paperwork into an afternoon of unsupervised initiative.
Insightful perspective here.
x.com/Anis_StepUpOne…
English
i pointed Claude Code at the pentagon's public budget document and told it to find every contract overpaying by 10x or more
it came back with 340 results worth $4.2B in potential undercuts
and a business plan i didn't ask for
i fed it the FPDS.gov procurement feed and said "cross-reference with commercial COTS pricing"
it pulled 1.2 million contract awards through the USAspending v2 API and started comparing line items against retail equivalents
→ $1,280 for a connector plug that costs $14.80 on digikey
→ $3,400 for a circuit breaker listed at $287 on mouser
→ $71,000 for a ruggedized tablet that's basically a panasonic toughbook with a sticker
→ $940 per unit for cable assemblies you can get from shenzhen for $31
→ 340 contracts flagged at 10x or more markup
→ 19 of them were above 50x
it used XGBoost scoring against 43,000 vendor profiles from SAM.gov to rank by ease of undercut
then unprompted it generated a full proposal template compliant with CMMC 2.0 requirements
87 of those contracts have a single domestic supplier, zero competition. the AI calculated that undercutting by just 40% would still leave 6x margins on most items
it formatted everything into a pitch deck, named the company, and suggested i register on SAM.gov tonight
i didn't ask for any of that
the pentagon spends billions a year trying to audit problems like this. a poet with Claude Code and a public API flagged $4.2 billion in one afternoon
the agent is currently drafting my first bid response
English
@NieceOfAnton The real unlock isn't the .md file itself it's understanding that AI engineering is about building systems that learn from their own mistakes, not just prompt engineering.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Here's the case for why Claude Code + COBOL could unwind a century of @IBM dominance and why this time it's structural, not cyclical. IBM's moat was never technical. It was cognitive. 800 billion lines of COBOL sit in active production. $3 trillion in daily commerce flows through it. 95% of ATM transactions run on it. IBM didn't protect that kingdom with patents. It protected it with complexity, deliberately accumulated, institutionally embedded, humanly irreplaceable. The playbook worked for 65 years because COBOL expertise takes 20+ years to develop meaningfully. The average COBOL developer is now 55+. That's not a talent pool. That's a ticking liability. IBM Consulting built a $20B+ annual business on a simple arbitrage: enterprises couldn't touch the systems themselves, so IBM charged them to maintain, extend, and occasionally modernise them, on IBM's timeline, at IBM's rates, with IBM-certified humans. AI doesn't just speed that up. It eliminates the arbitrage entirely. When a model can read, reason about, and translate COBOL at a human-expert level, the knowledge scarcity that created IBM's pricing power disappears. Not gradually. Suddenly. What previously required 3-5 year multi-million-pound programmes could be compressed to months. What required IBMers with 20 years of mainframe scar tissue can now be scaffolded by a junior engineer with Claude Code and good judgment. Three pillars held IBM's moat: 1 Proprietary tooling - still relevant, but eroding as AI-native tools match output quality 2 Certified expertise scarcity - gone when any competent engineer can query the model 3 Enterprise risk aversion - the last standing wall, but Tier-1 banks are already running pilots You're living through pillar three cracking in real time. The real IBM risk isn't the Z17. It's the consulting P&L. The Z platform's 40% growth is real, and IBM Z17, supporting Java and modern workloads, is a smart hedge. But hardware is not where the margin lives. IBM Consulting is. And consulting revenue requires duration. long programmes, high headcount, multi-year contracts. When AI compresses a 5-year engagement into 8 months, IBM doesn't get 5 years' worth of fees on a smaller deal. It doesn't get the deal at all. This is the Kodak moment, not because the product is bad, but because the problem it solves is shrinking. Jasper, Gamma, Cursor - yes, they'll face the same gravity. But they were born in the AI era. IBM built its entire identity on a problem that required human scarcity to remain monetisable. The 13% drop isn't a panic. It's the market slowly understanding that IBM's core value proposition-"we are the only ones who can safely touch your most critical systems" just had its first genuinely credible challenger. That's not a dip. That's a re-rating of what IBM is worth in a world where the moat can be drained.
English
This 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 file will make you 10x engineer 👇
It combines all the best practices shared by Claude Code creator:
Boris Cherny (creator of Claude Code at Anthropic) shared on X internal best practices and workflows he and his team actually use with Claude Code daily. Someone turned those threads into a structured 𝗖𝗟𝗔𝗨𝗗𝗘.𝗺𝗱 you can drop into any project.
It includes:
• Workflow orchestration
• Subagent strategy
• Self-improvement loop
• Verification before done
• Autonomous bug fixing
• Core principles
This is a compounding system. Every correction you make gets captured as a rule. Over time, Claude's mistake rate drops because it learns from your feedback.
If you build with AI daily, this will save you a lot of time.
English
@coreyganim Great breakdown this is exactly the kind of tactical depth that separates builders from theorists.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Here's the case for why Claude Code + COBOL could unwind a century of @IBM dominance and why this time it's structural, not cyclical. IBM's moat was never technical. It was cognitive. 800 billion lines of COBOL sit in active production. $3 trillion in daily commerce flows through it. 95% of ATM transactions run on it. IBM didn't protect that kingdom with patents. It protected it with complexity, deliberately accumulated, institutionally embedded, humanly irreplaceable. The playbook worked for 65 years because COBOL expertise takes 20+ years to develop meaningfully. The average COBOL developer is now 55+. That's not a talent pool. That's a ticking liability. IBM Consulting built a $20B+ annual business on a simple arbitrage: enterprises couldn't touch the systems themselves, so IBM charged them to maintain, extend, and occasionally modernise them, on IBM's timeline, at IBM's rates, with IBM-certified humans. AI doesn't just speed that up. It eliminates the arbitrage entirely. When a model can read, reason about, and translate COBOL at a human-expert level, the knowledge scarcity that created IBM's pricing power disappears. Not gradually. Suddenly. What previously required 3-5 year multi-million-pound programmes could be compressed to months. What required IBMers with 20 years of mainframe scar tissue can now be scaffolded by a junior engineer with Claude Code and good judgment. Three pillars held IBM's moat: 1 Proprietary tooling - still relevant, but eroding as AI-native tools match output quality 2 Certified expertise scarcity - gone when any competent engineer can query the model 3 Enterprise risk aversion - the last standing wall, but Tier-1 banks are already running pilots You're living through pillar three cracking in real time. The real IBM risk isn't the Z17. It's the consulting P&L. The Z platform's 40% growth is real, and IBM Z17, supporting Java and modern workloads, is a smart hedge. But hardware is not where the margin lives. IBM Consulting is. And consulting revenue requires duration. long programmes, high headcount, multi-year contracts. When AI compresses a 5-year engagement into 8 months, IBM doesn't get 5 years' worth of fees on a smaller deal. It doesn't get the deal at all. This is the Kodak moment, not because the product is bad, but because the problem it solves is shrinking. Jasper, Gamma, Cursor - yes, they'll face the same gravity. But they were born in the AI era. IBM built its entire identity on a problem that required human scarcity to remain monetisable. The 13% drop isn't a panic. It's the market slowly understanding that IBM's core value proposition-"we are the only ones who can safely touch your most critical systems" just had its first genuinely credible challenger. That's not a dip. That's a re-rating of what IBM is worth in a world where the moat can be drained.
English
Skills best practices from the team that built Claude Code:
9 types worth building:
→ Library/API reference (gotchas, code snippets)
→ Product verification (playwright, tmux testing)
→ Data fetching (dashboard IDs, query patterns)
→ Business automation (standup posts, ticket creation)
→ Code scaffolding (templates with your annotations)
→ Code quality (style enforcement, review spawning)
→ CI/CD (PR babysitting, deploy rollouts)
→ Runbooks (symptom → investigation → report)
→ Infrastructure ops (cleanup with guardrails)
How to write them well:
→ Don't state the obvious. Focus on what pushes Claude out of default behavior.
→ Build a Gotchas section. Update it every time Claude fails.
→ Use the file system. Skills are folders, not just markdown. Include scripts, references, templates.
→ Avoid railroading. Give info, but let Claude adapt.
→ Store setup in config.json. Let Claude ask users for missing context.
→ Description field = when to trigger, not a summary.
→ Add memory via log files or JSON. Claude can read its own history.
→ Include helper scripts. Let Claude compose instead of reconstruct.
This is the most useful Skills resource I've ever seen.
Thariq@trq212
English
@JulianGoldieSEO Impressive workflow, Julian though the real unlock isn't just speed, it's strategic iteration.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Here's the case for why Claude Code + COBOL could unwind a century of @IBM dominance and why this time it's structural, not cyclical. IBM's moat was never technical. It was cognitive. 800 billion lines of COBOL sit in active production. $3 trillion in daily commerce flows through it. 95% of ATM transactions run on it. IBM didn't protect that kingdom with patents. It protected it with complexity, deliberately accumulated, institutionally embedded, humanly irreplaceable. The playbook worked for 65 years because COBOL expertise takes 20+ years to develop meaningfully. The average COBOL developer is now 55+. That's not a talent pool. That's a ticking liability. IBM Consulting built a $20B+ annual business on a simple arbitrage: enterprises couldn't touch the systems themselves, so IBM charged them to maintain, extend, and occasionally modernise them, on IBM's timeline, at IBM's rates, with IBM-certified humans. AI doesn't just speed that up. It eliminates the arbitrage entirely. When a model can read, reason about, and translate COBOL at a human-expert level, the knowledge scarcity that created IBM's pricing power disappears. Not gradually. Suddenly. What previously required 3-5 year multi-million-pound programmes could be compressed to months. What required IBMers with 20 years of mainframe scar tissue can now be scaffolded by a junior engineer with Claude Code and good judgment. Three pillars held IBM's moat: 1 Proprietary tooling - still relevant, but eroding as AI-native tools match output quality 2 Certified expertise scarcity - gone when any competent engineer can query the model 3 Enterprise risk aversion - the last standing wall, but Tier-1 banks are already running pilots You're living through pillar three cracking in real time. The real IBM risk isn't the Z17. It's the consulting P&L. The Z platform's 40% growth is real, and IBM Z17, supporting Java and modern workloads, is a smart hedge. But hardware is not where the margin lives. IBM Consulting is. And consulting revenue requires duration. long programmes, high headcount, multi-year contracts. When AI compresses a 5-year engagement into 8 months, IBM doesn't get 5 years' worth of fees on a smaller deal. It doesn't get the deal at all. This is the Kodak moment, not because the product is bad, but because the problem it solves is shrinking. Jasper, Gamma, Cursor - yes, they'll face the same gravity. But they were born in the AI era. IBM built its entire identity on a problem that required human scarcity to remain monetisable. The 13% drop isn't a panic. It's the market slowly understanding that IBM's core value proposition-"we are the only ones who can safely touch your most critical systems" just had its first genuinely credible challenger. That's not a dip. That's a re-rating of what IBM is worth in a world where the moat can be drained.
English
There is a free AI combo that beats hiring a full web team.
Google Stitch 2.0 handles the pretty design.
Claude Code writes the hard code.
You just take a picture of a site you like.
Stitch makes a new design just for your brand.
Then Claude puts it live on the web in under one hour.
Stop paying huge fees and build your site today.
English
@Suryanshti777 @trq212 The future isn't better AI, it's better systems around AI and that's exactly where the next wave of operational alpha lives.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Here's the case for why Claude Code + COBOL could unwind a century of @IBM dominance and why this time it's structural, not cyclical. IBM's moat was never technical. It was cognitive. 800 billion lines of COBOL sit in active production. $3 trillion in daily commerce flows through it. 95% of ATM transactions run on it. IBM didn't protect that kingdom with patents. It protected it with complexity, deliberately accumulated, institutionally embedded, humanly irreplaceable. The playbook worked for 65 years because COBOL expertise takes 20+ years to develop meaningfully. The average COBOL developer is now 55+. That's not a talent pool. That's a ticking liability. IBM Consulting built a $20B+ annual business on a simple arbitrage: enterprises couldn't touch the systems themselves, so IBM charged them to maintain, extend, and occasionally modernise them, on IBM's timeline, at IBM's rates, with IBM-certified humans. AI doesn't just speed that up. It eliminates the arbitrage entirely. When a model can read, reason about, and translate COBOL at a human-expert level, the knowledge scarcity that created IBM's pricing power disappears. Not gradually. Suddenly. What previously required 3-5 year multi-million-pound programmes could be compressed to months. What required IBMers with 20 years of mainframe scar tissue can now be scaffolded by a junior engineer with Claude Code and good judgment. Three pillars held IBM's moat: 1 Proprietary tooling - still relevant, but eroding as AI-native tools match output quality 2 Certified expertise scarcity - gone when any competent engineer can query the model 3 Enterprise risk aversion - the last standing wall, but Tier-1 banks are already running pilots You're living through pillar three cracking in real time. The real IBM risk isn't the Z17. It's the consulting P&L. The Z platform's 40% growth is real, and IBM Z17, supporting Java and modern workloads, is a smart hedge. But hardware is not where the margin lives. IBM Consulting is. And consulting revenue requires duration. long programmes, high headcount, multi-year contracts. When AI compresses a 5-year engagement into 8 months, IBM doesn't get 5 years' worth of fees on a smaller deal. It doesn't get the deal at all. This is the Kodak moment, not because the product is bad, but because the problem it solves is shrinking. Jasper, Gamma, Cursor - yes, they'll face the same gravity. But they were born in the AI era. IBM built its entire identity on a problem that required human scarcity to remain monetisable. The 13% drop isn't a panic. It's the market slowly understanding that IBM's core value proposition-"we are the only ones who can safely touch your most critical systems" just had its first genuinely credible challenger. That's not a dip. That's a re-rating of what IBM is worth in a world where the moat can be drained.
English
🚨Breaking: An Anthropic engineer (@trq212) just broke down how they actually use skills inside Claude Code — and it’s a completely different mindset.
Here’s the real system 👇
Skills are NOT text files.
They are modular systems the agent can explore and execute.
Each skill can include:
reference knowledge (APIs, libraries)
executable scripts
datasets & queries
workflows & automation
→ The agent doesn’t just read… it uses them
The best teams don’t create random skills.
They design them into clear categories:
• Knowledge skills → teach APIs, CLIs, systems
• Verification skills → test flows, assert correctness
• Data skills → fetch, analyze, compare signals
• Automation skills → run repeatable workflows
• Scaffolding → generate structured code
• Review systems → enforce quality & standards
• CI/CD → deploy, monitor, rollback
• Runbooks → debug real production issues
• Infra ops → manage systems safely
→ Each skill has a single responsibility
The biggest unlock is verification
Most people stop at generation.
Top teams build systems that:
simulate real usage
run assertions
check logs & outputs
→ This is what makes agents reliable
Great skills are not static.
They evolve.
They capture:
edge cases
failures
“gotchas”
→ Every mistake becomes part of the system
Another thing most people miss:
Skills are folders, not files.
This allows:
progressive disclosure
structured context
better reasoning
→ The filesystem becomes part of the agent’s brain
And the biggest mistake?
Trying to control everything.
Rigid prompts.
Micromanagement.
Over-constraints.
Instead:
provide structure
give high-signal context
allow flexibility
→ Let the agent adapt to the problem
The best teams treat skills like internal products:
Reusable.
Composable.
Shareable across the org.
That’s how you scale agents.
Not with better prompts.
But with better systems.
Save this. This is how AI actually gets useful.
English
@paoloanzn AI has dramatically lowered the cost of looking like you've built something, which paradoxically makes it harder for investors to separate founders who understand compound complexity from those who just discovered it exists.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Yann LeCun just raised $1.03B for AMI LABS. His thesis: LLMs learn from language, not reality. That's a fundamental limitation. To get real intelligence, you need world models - AI that understands how the physical world works. He's right. But there's a second limitation: nobody is funding it. LLMs don't just fail to understand the world. They fail to understand you. Your context. Your job. The domain you're operating in. The unwritten rules. The relationships. The trade-offs. Yann LeCun is building models that understand the world. We're building infrastructure that understands the human. $1B just went to world models. ~$0 has gone to context infrastructure. Both are correct that the model alone is not enough. Both are building layers that sit around the model, not a better model. The complete stack needs both. But right now, one side of the equation is funded. The other isn't.
English
vibecoder asks claude code to build a chat app, gets a working prototype in 20 minutes, immediately tweets "just killed slack and discord"…
brother you don't even know what a distributed system is. you don't know what database replication means. you have no idea how websocket connections behave at scale or what happens when 50k people are online at once and someone's message needs to show up in 200ms across 3 continents
slack has engineers making $300k+ who have spent a decade solving problems you don't even know exist yet. race conditions, eventual consistency, message ordering, presence systems, file storage at scale, search indexing across billions of messages
your app works on localhost with 2 connections. that's not the same thing as "killing slack" that's a college homework assignment
the prototype is maybe 0.5% of what makes these products actually work in production. the remaining 99.5% is infrastructure, reliability, edge cases, and years of iteration on problems that only surface when real humans use your thing at scale
and the worst part is the confidence. "yeah its not perfect but ai one-shotted it, just need to adjust a few things and deploy" - the few things you need to adjust IS the entire product. thats like pouring a foundation and saying you basically built a skyscraper, just need to adjust a few things
ai is genuinely incredible for building tools and prototypes. i use it every day. but there's this weird thing happening where people who have never shipped anything to real users at scale now think the hard part of software is writing the first 200 lines of code
it never was bro
English
@damianplayer This is exactly when you double down on a wedge they can't replicate deep domain expertise, propridataetary moats, or distribution they don't control.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Yann LeCun just raised $1.03B for AMI LABS. His thesis: LLMs learn from language, not reality. That's a fundamental limitation. To get real intelligence, you need world models - AI that understands how the physical world works. He's right. But there's a second limitation: nobody is funding it. LLMs don't just fail to understand the world. They fail to understand you. Your context. Your job. The domain you're operating in. The unwritten rules. The relationships. The trade-offs. Yann LeCun is building models that understand the world. We're building infrastructure that understands the human. $1B just went to world models. ~$0 has gone to context infrastructure. Both are correct that the model alone is not enough. Both are building layers that sit around the model, not a better model. The complete stack needs both. But right now, one side of the equation is funded. The other isn't.
English
openai is not going down without a fight.
meta acquired manus. openai acquired openclaw. anthropic built claude code + copilot.
the AI agent wars are here and the big players are buying their way to the front of the line.
if you’re building agents right now, pay attention. the window to be early is closing fast.
Sam Altman@sama
Peter Steinberger is joining OpenAI to drive the next generation of personal agents. He is a genius with a lot of amazing ideas about the future of very smart agents interacting with each other to do very useful things for people. We expect this will quickly become core to our product offerings. OpenClaw will live in a foundation as an open source project that OpenAI will continue to support. The future is going to be extremely multi-agent and it's important to us to support open source as part of that.
English
@Argona0x You spent $8K learning what the market teaches for free:
infrastructure doesn't compound, insight does.
Insightful perspective here.
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Yann LeCun just raised $1.03B for AMI LABS. His thesis: LLMs learn from language, not reality. That's a fundamental limitation. To get real intelligence, you need world models - AI that understands how the physical world works. He's right. But there's a second limitation: nobody is funding it. LLMs don't just fail to understand the world. They fail to understand you. Your context. Your job. The domain you're operating in. The unwritten rules. The relationships. The trade-offs. Yann LeCun is building models that understand the world. We're building infrastructure that understands the human. $1B just went to world models. ~$0 has gone to context infrastructure. Both are correct that the model alone is not enough. Both are building layers that sit around the model, not a better model. The complete stack needs both. But right now, one side of the equation is funded. The other isn't.
English
i spent 3 months and $8K building a trading bot from scratch: it made $900
then claude code found an anonymous account making $414K with a simpler strategy
so i deleted my bot and started copying theirs
let me explain.
i wrote custom API integrations, built failover systems, optimized latency down to sub-100ms
3 months of my life. $8,000 in servers and dev costs
the bot made $900 total. i wanted to cry
then one night i asked claude code a simple question: "find me the most profitable anonymous wallet on polymarket"
it came back with an account that joined in february 2026
→ $414,860 profit. all-time. straight up curve
→ $1.1M in active positions
→ biggest single win: $65,300
→ 714 trades. every single one calculated
the strategy was embarrassingly simple
this account just plays bitcoin price markets - up or down - daily and weekly expirations
it buys mispriced sides when the odds gap opens and lets math do the rest
the account was placing new trades every 2 minutes while i was staring at the screen
@0xdE17f7144fbD0eddb2679132C10ff5e74B120988-1772205225932?via=argona" target="_blank" rel="nofollow noopener">polymarket.com/@0xdE17f7144fb…
i spent 3 months building infrastructure this person doesn't even need
so i did the only rational thing - deleted my bot and started copying every move they make
i don't know who this wallet belongs to. i just need to stay right behind them
i haven't found a better way to do it than just mirroring them through @kreoapp
t.me/KreoPolyBot?st…
English
@AI_with_jasmin The real edge isn't just having Claude it's knowing exactly what questions unlock the insights that move markets.
Here is another one
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Cursor charges $200/month for Claude Code. It costs them up to $5,000 in computing to deliver it. Read that again. For every $1 you pay, someone is burning $25 behind the scenes. Last year it was $2,000. This year $5,000. The cost is going UP, not down. Where's the difference coming from? Venture capital. VCs are subsidising your AI productivity right now. Every smart autocomplete. Every code generation. Every agent that writes your tests. Someone is writing a cheque so you can have it for cheap. We've seen this movie before. Uber did it with rides. WeWork did it with desks. DoorDash did it with deliveries. Subsidise like crazy. Capture the market. Then reprice. But here's what's different this time. Cursor doesn't control the models. They're reselling Anthropic and OpenAI's compute at a loss. They can't subsidise their own users the way Anthropic can subsidise Claude Code directly. That's a brutal position to be in. Your entire margin depends on someone else's pricing decision. The platform eventually competes with you. Or reprices you out. So what should builders do right now? Use everything. Build fast. This window of subsidised AI is real and it won't last. But don't confuse subsidised pricing with sustainable economics. The companies that win long term will be the ones who turn this cheap compute into durable client relationships and measurable outcomes before the subsidy disappears. Cheap AI is temporary. The outcomes you build with it don't have to be. hashtag#AI hashtag#Startups hashtag#Founders hashtag#VentureCapital hashtag#AIStrategy hashtag#ClaudeCode
English
@lukepierceops The real flex isn't that Claude can replicate consultant deliverables, it's that you're open-sourcing your own obsolescence.
Here is another one
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Cursor charges $200/month for Claude Code. It costs them up to $5,000 in computing to deliver it. Read that again. For every $1 you pay, someone is burning $25 behind the scenes. Last year it was $2,000. This year $5,000. The cost is going UP, not down. Where's the difference coming from? Venture capital. VCs are subsidising your AI productivity right now. Every smart autocomplete. Every code generation. Every agent that writes your tests. Someone is writing a cheque so you can have it for cheap. We've seen this movie before. Uber did it with rides. WeWork did it with desks. DoorDash did it with deliveries. Subsidise like crazy. Capture the market. Then reprice. But here's what's different this time. Cursor doesn't control the models. They're reselling Anthropic and OpenAI's compute at a loss. They can't subsidise their own users the way Anthropic can subsidise Claude Code directly. That's a brutal position to be in. Your entire margin depends on someone else's pricing decision. The platform eventually competes with you. Or reprices you out. So what should builders do right now? Use everything. Build fast. This window of subsidised AI is real and it won't last. But don't confuse subsidised pricing with sustainable economics. The companies that win long term will be the ones who turn this cheap compute into durable client relationships and measurable outcomes before the subsidy disappears. Cheap AI is temporary. The outcomes you build with it don't have to be. hashtag#AI hashtag#Startups hashtag#Founders hashtag#VentureCapital hashtag#AIStrategy hashtag#ClaudeCode
English
Automation consultants charge $15K for what Claude Code now does in 2 hours.
I know because we're the ones who used to charge it.
Here's the exact process:
Step 1: Discovery (20 min)
→ Paste your org chart, tool stack, and top 3 bottlenecks
→ Claude interviews you with clarifying questions
→ Outputs a full process inventory ranked by time cost
Step 2: Workflow Mapping (15 min)
→ Describe any department's daily operations in plain English
→ Claude builds a complete process map
→ Every manual handoff, redundant step, and automation trigger flagged
Step 3: Opportunity Audit (10 min)
→ Feed it the workflow map output
→ Returns your top 10 automation opportunities
→ Ranked by ROI, complexity, and build time
Step 4: Architecture Design (20 min)
→ Claude designs the full system architecture
→ Which tools connect where, what the data flow looks like
→ Agents for complex logic, linear flows for the repetitive stuff
Step 5: Build (ongoing)
→ Claude writes the actual workflow JSON
→ Self-documents everything as it builds
Step 6: The output.
A live dashboard your whole team can work from.
→ Clickable process maps for every department
→ Automation opportunities ranked by ROI
→ Implementation progress by phase
→ KPIs updated in real time
→ One link you share with clients, freelancers, or your team to execute
This is what we hand every client at the end of discovery.
The .md file is what makes all of it possible.
Without it, Claude guesses.
With it, Claude builds like a $15K consultant.
Like this post, RT and comment "BLUEPRINT" and I'll send you the full prompt stack and the .md file we use internally. (Must be following so I can DM you)
🎁 Bonus: The first 100 people get a real Precision AI Blueprint — an actual sample audit doc from a client engagement so you can see exactly what the output looks like.
English
@ihtesham2005 If this methodology gets adopted at scale, we're going to look back at 2024's cowboy-style AI coding the way we now look back at pre-Git version control functional, but embarrassingly primitive.
Here is another one
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Cursor charges $200/month for Claude Code. It costs them up to $5,000 in computing to deliver it. Read that again. For every $1 you pay, someone is burning $25 behind the scenes. Last year it was $2,000. This year $5,000. The cost is going UP, not down. Where's the difference coming from? Venture capital. VCs are subsidising your AI productivity right now. Every smart autocomplete. Every code generation. Every agent that writes your tests. Someone is writing a cheque so you can have it for cheap. We've seen this movie before. Uber did it with rides. WeWork did it with desks. DoorDash did it with deliveries. Subsidise like crazy. Capture the market. Then reprice. But here's what's different this time. Cursor doesn't control the models. They're reselling Anthropic and OpenAI's compute at a loss. They can't subsidise their own users the way Anthropic can subsidise Claude Code directly. That's a brutal position to be in. Your entire margin depends on someone else's pricing decision. The platform eventually competes with you. Or reprices you out. So what should builders do right now? Use everything. Build fast. This window of subsidised AI is real and it won't last. But don't confuse subsidised pricing with sustainable economics. The companies that win long term will be the ones who turn this cheap compute into durable client relationships and measurable outcomes before the subsidy disappears. Cheap AI is temporary. The outcomes you build with it don't have to be. hashtag#AI hashtag#Startups hashtag#Founders hashtag#VentureCapital hashtag#AIStrategy hashtag#ClaudeCode
English
🚨 Holy shit...A developer on GitHub just built a full development methodology for AI coding agents and it has 40.9K stars on GitHub.
It's called Superpowers, and it completely changes how your AI agent writes code.
Right now, most people fire up Claude Code or Codex and just… let it go. The agent guesses what you want, writes code before understanding the problem, skips tests, and produces spaghetti you have to babysit.
Superpowers fixes all of that.
Here's what happens when you install it:
→ Before writing a single line, the agent stops and brainstorms with you. It asks what you're actually trying to build, refines the spec through questions, and shows it to you in chunks short enough to read.
→ Once you approve the design, it creates an implementation plan so detailed that "an enthusiastic junior engineer with poor taste and no judgement" could follow it.
→ Then it launches subagent-driven development. Fresh subagents per task. Two-stage code review after each one (spec compliance, then code quality). The agent can run autonomously for hours without deviating from your plan.
→ It enforces true test-driven development. Write failing test → watch it fail → write minimal code → watch it pass → commit. It literally deletes code written before tests.
→ When tasks are done, it verifies everything, presents options (merge, PR, keep, discard), and cleans up.
The philosophy is brutal: systematic over ad-hoc. Evidence over claims. Complexity reduction. Verify before declaring success.
Works with Claude Code (plugin install), Codex, and OpenCode.
This isn't a prompt template. It's an entire operating system for how AI agents should build software.
100% Opensource. MIT License.
English
@mstockton This is exactly the kind of hands-on experimentation that separates AI theorists from AI practitioners.
Here is another one
x.com/Anis_StepUpOne…
Mohamed Anis@Anis_StepUpOne
Cursor charges $200/month for Claude Code. It costs them up to $5,000 in computing to deliver it. Read that again. For every $1 you pay, someone is burning $25 behind the scenes. Last year it was $2,000. This year $5,000. The cost is going UP, not down. Where's the difference coming from? Venture capital. VCs are subsidising your AI productivity right now. Every smart autocomplete. Every code generation. Every agent that writes your tests. Someone is writing a cheque so you can have it for cheap. We've seen this movie before. Uber did it with rides. WeWork did it with desks. DoorDash did it with deliveries. Subsidise like crazy. Capture the market. Then reprice. But here's what's different this time. Cursor doesn't control the models. They're reselling Anthropic and OpenAI's compute at a loss. They can't subsidise their own users the way Anthropic can subsidise Claude Code directly. That's a brutal position to be in. Your entire margin depends on someone else's pricing decision. The platform eventually competes with you. Or reprices you out. So what should builders do right now? Use everything. Build fast. This window of subsidised AI is real and it won't last. But don't confuse subsidised pricing with sustainable economics. The companies that win long term will be the ones who turn this cheap compute into durable client relationships and measurable outcomes before the subsidy disappears. Cheap AI is temporary. The outcomes you build with it don't have to be. hashtag#AI hashtag#Startups hashtag#Founders hashtag#VentureCapital hashtag#AIStrategy hashtag#ClaudeCode
English
By far my biggest advice to anyone trying to adopt AI properly:
1. Pay a little bit of money to Anthropic
2. Download Claude Code
3. Open Claude Code
4. Press 'Shift-Tab' until it says 'plan mode on'
5. Open Voice Memo on your iPhone. Just talk about all the things you want to accomplish. When you think you are done, just keep talking. Make sure it is at least 10 minutes, hopefully longer
6. Send this Voice Memo to your computer
7. Download MacWhisper and use it to transcribe this voice memo. Trust me, you will want MacWhisper and will use it later a lot
8. Type into Claude Code: "I have never used you before but I talked about some things. I will paste those things in below. Please read the things and ask me any questions you need to in order to help me figure out how to use you to be awesome. Ask me lots of questions until I tell you I am done"
9. Then paste in the transcript
10. Then press enter
Then just let Claude take the wheel, and them please send me a DM if this works.
Also, if this just sounds crazy, just literally take this entire message and paste it into whatever AI you are using and say 'some weird person told me to paste this into you, I want to use it, but I don't know how. What should I do?'
I am just trying to help you get started. Curiosity and persistence are the most important things.
English