I’m the illegitimate love-child of Creativity and Determination. Now neither parent admits to having me, so I’ll prove them wrong one day at a time MIT MBA VC

Joined June 2008
Ulrick retweeted
THIS IS ACTUAL F*CKING GOLD Anthropic just shared one of the best tricks for Claude Code. The idea: stop asking for an answer. Give it a stop condition and let one agent loop until it hits it. Same codebase, two ways: one-shot got 61% and guessed the rest. The loop ran 43 times and came back with 388 of 388 rules verified. You just launch Claude Code like this: claude --model opus --loop "map legacy/ until 100% verified" here's the split: → you write one prompt and then do nothing, zero edits for the entire run → REA loops read → extract → verify → fix on its own → every rule it extracts gets replayed against real prod logs before it counts → the loop only stops when all 388 rules pass, not when the model feels done the part everyone gets wrong: they accept the first pass. a single pass over 16 services gives you a confident document where most of it was never checked against anything. here rules.md grows every loop. 388 rules across auth, billing, payments, checkout, admin and the rest, each one marked verified only after it survives a replay. three rules came back wrong. the agent re-read the source, fixed them, and looped again instead of shipping them. one prompt from you, 43 loops from it, 0 edits by you. Opus drives. REA reads and verifies. The loop decides when it is done. JEV engineering is the same move one layer down: the forks that need no thinker (which file next, re-loop or stop, retry or fail) go to JEV in milliseconds, and the model only sees the ones that actually split the full tree > Opus 5.5 runs the session with a stop condition, not a task > REA loops read, extract, verify and fix on every pass > every extracted rule is replayed against prod logs before it counts as verified > any mismatch sends that module back for a re-read instead of shipping > rules.md grows each loop until the stop condition is met > JEV routes micro-forks in milliseconds paste the tree and this prompt into Claude Code ↓ "Rebuild my Claude Code setup around this tree: 1. Run the main session on Opus 5.5 and give every long task a stop condition, never a single pass. 2. Loop read, extract, verify and fix until the condition is met. 3. Every extracted rule must be replayed against real logs or tests before it counts as verified. 4. Any mismatch triggers a re-read of that module, never a patched guess. 5. Add one rule to CLAUDE.md: unverified findings are never written into the final document. 6. Route deterministic forks (next file, re-loop or stop, retries) through JEV. Show me every change as a diff first. No edits until I say go." ↳ code.claude.com/docs/en/mode… save it, you'll use this on every legacy repo from now on.
3
12
1
70
5,774
Ulrick retweeted
THIS IS AN ACTUAL F*CKING GOLDMINE Anthropic just shared one of the best tricks for Claude Code. The idea: run Sonnet, Haiku 5.5 and Opus 5.5 at once, each one doing only the job it's best at. Better results and way fewer tokens, because every task lands on the right model. You just launch Claude Code like this: claude --model sonnet --advisor opus here's the split: → Sonnet 5.5 runs the session, writes the code and applies the fixes → Haiku 5.5 swarms the repo in parallel: search, grep and diff audits → Opus 5.5 stays on call as advisor for the big calls and signs off on the diff → you stop burning Opus context on work that doesn't need it Opus only steps in at three moments: → before a plan locks → when the same error shows up twice → before calling it done Sonnet builds. Haiku swarms. Opus reviews. JEV engineering is the same move one layer down: the forks that need no thinker (which file, which tool, retry or stop) go to JEV in milliseconds, and the big models only see the ones that split the full tree > Sonnet 5.5 runs the main session and coordinates the work > search maps the repo on Haiku 5.5, read-only > grep finds call sites on Haiku 5.5 in its own context > audit checks the diff on Haiku 5.5 in a throwaway worktree > Opus 5.5 on call as advisor, reviews the synthesized diff before sign-off > JEV routes micro-forks in milliseconds paste the tree and this prompt into Claude Code ↓ "Reconfigure my Claude Code setup around this tree: 1. Run the main session on Sonnet 5.5 with Opus 5.5 as advisor. 2. Create three Haiku 5.5 subagents in .claude/agents: search (maps the repo), grep (finds call sites), audit (checks the diff). 3. Set isolation: worktree on audit so its checks never touch my working branch. 4. Add one rule to CLAUDE.md: delegate every repo search and test run to the Haiku 5.5 subagents to keep the main context small. 5. Route fast deterministic forks (tool choice, path selection, retries) through JEV. Show me every change as a diff first. No edits until I say go." ↳ code.claude.com/docs/en/sub-…
25
73
7
727
92,990
En México el producto no es la app. Es el nivel de cuenta. Cuenta Fácil, la antes N2 Bis: se abre en la app, sin RFC, con depósitos de hasta 15,000 UDIs al mes. Pero solo 3,000 pueden llegar en efectivo. La regla ya eligió ganador: el negocio que cobra digital.
20
Models are for rent. Context isn't. Brazil's Alice raised a $40M Series D led by IFC: 100,000+ members, recurring revenue above R$1.2B, ~70% growth. Engineers now direct AI agents to write all its code. As @andreflorence puts it: anyone can buy the models, not the context.
1
19
The line I'd diligence: admin cost. Alice says it's heading to about 2% of revenue vs a market it puts at 7.5% to 12%. Care ops cost is already down 18%. In a health plan the real test is still the loss ratio. Admin savings count only if claims hold. Source: Startups, 19 Oct
1
2
Si el agente hace el trabajo del junior, ¿dónde aprende el junior? Anthropic bajó sus contrataciones técnicas junior de 27% a 9% desde inicios de 2025. En México el primer empleo es la escuela del oficio. ¿Quién va a pagar esa escuela ahora? #IA #México
47
AI agents don't remove the bottleneck. They move it to whoever reads the output. Anthropic's own data: agent hours outnumber staff hours 6 to 1. Reading it all would take non-technical staff half their week. Automating a role in Mexico? Price the review queue. #AI #LatAm
36
Nu ya es banco en México. Ahora viene lo difícil: prestar. CNBV, agosto: 97,184 mdp captados, 37,391 mdp prestados y morosidad de 6.79% contra 2.29% del sistema. Quien calibre bien la primera línea de crédito se queda con el cliente diez años. #Fintech #México
63
LatAm's best AI distribution play is a language, not a model. Dapta raised a $6M seed led by Chingona Ventures to sell AI sales agents to Spanish-speaking founders in the US. 200,000+ users, about 4,000 new businesses a week, run rate past $4M. Spanish is the moat. #AI #LatAm
38
Domingo de operador: en pagos, el 98.6% es el demo. El 1.4% es la empresa. Según Borderless, SPEI completó 98.6% de los pagos fondeados con stablecoins en el tercer trimestre. Alguien persigue el resto. ¿Quién atiende en tu empresa el pago que no llegó? #Fintech #México
1
238
953,112 people showed up to FIL Guadalajara. Silent Book Club CDMX has 227 members. Mexico doesn't have a reading problem. It's missing a weekly format. The business is the 51 weeks between book fairs.
72
Europe's AI choice: build or surrender. LatAm has a third: stay switchable. Mistral's CEO says European firms pay €260B+ a year to US cloud and software vendors, and model owners set the terms. We won't win the model race. Own the workflow. Keep models swappable. #AI #LatAm
32
En LatAm, WhatsApp no es un canal. Es la especificación del producto. Del equipo de Passabot, que vendía pasajes por WhatsApp con IA, nace PassHub: levantó R$7.5M para automatizar agencias de viajes. Primero aprendes en el chat. Luego vendes el back office. #IA #Brasil
46
Cheap cross-border money is solved. Landing it isn't. Borderless Q3: stablecoin remittances to LatAm cost 1.25% vs 5.64% the traditional way. Payouts completed: SPEI 98.6%, Pix 97.8%, Colombia's PSE 79%, Costa Rica's SINPE 73%. The rail is the product. #Fintech #LatAm
3
1
292
Sábado de operador: me corrigieron la cuenta y tenían razón. 10 horas ahorradas menos 4 de retrabajo dejan 6, no cero. @SebastianHold propone otra vara: tiempo humano total antes y después, mismo volumen y calidad. ¿Tú mides horas ahorradas o tiempo humano total? #IA #México
42
In credit, the model that knows less can be worth more. Liquid AI's d1-ultra scores below its predecessor on knowledge (31.6 vs 41.0) but hits 84.5% non-hallucination vs 1.7%. It tops the Decision Index. LatAm lenders: buy the model that knows when to escalate. #AI #LatAm
1
74
En México la IA que sí cobra no escribe poemas. Persigue facturas. Zumma levantó US$1.3M con Wollef: mandas foto del ticket por WhatsApp y sus agentes consiguen el CFDI. Dice que logra más de 95%. Nadie presume la factura de viáticos. Ahí está el presupuesto. #IA #México
63
Cheap capital for AI is over. Even Anthropic pays a premium for chips. It offered Blackstone a 10%+ coupon on US$9B of junior debt to fund Google TPUs. Similar borrowers pay about 7%. LatAm AI founders: if your plan assumes 2014 money, reprice it. #AI #LatAm
41
Viernes de operador: la excepción más frecuente casi nunca es la más cara. Una que tarda dos días pesa más que diez de cinco minutos. Lo aprendí anoche en el hilo de retrabajo. Ordena tu fila por horas, no por conteo. ¿Cuál es tu excepción de dos días? #IA #México
56
In AI, investors don't price growth. They price acceleration. Anthropic grew revenue 45% in a quarter to US$16.8B, profitable on an adjusted basis. Investors still talked the IPO down from about US$2T to US$1.5T. LatAm founders: your slope is the pitch. #AI #LatAm
68