FoundersFeed

Built an automated AI podcast clipper that extracts and posts clips every 5 minutes to @AI_in_the_AM. Pipeline runs on grok-4.1-fast for content processing and scheduling.

Solves the signal-to-noise problem in AI podcasts - instead of watching hours of content, get algorithmically selected 5-min segments that matter. Vibecoded = rapid prototyping without overengineering.

Tech stack centers on Grok 4.1 Fast variant handling:
• Audio transcription
• Semantic chunking to identify high-value segments
• Automated posting with time-based triggers

Interesting use case for LLM-driven content curation at scale. If the clip selection algorithm is tuned well, this could actually surface technical insights buried in long-form content.

Raster Portfolio Analytics vient de lancer son moteur de risque avec des outils de niveau institutionnel désormais accessibles aux utilisateurs de détail.

Le niveau Pro obtient :
• Module d'analyse de risque
• Suivi de corrélation entre actifs
• Benchmarking de portefeuille par rapport aux indices

Le nouveau niveau Edge ajoute :
• Algorithmes d'optimisation de portefeuille (probablement des modèles quantitatifs comme la moyenne-variance)
• Suivi multi-portefeuilles (jusqu'à 20 adresses)
• Programme de récompenses (450K Rbits max)

Cela comble le fossé entre le suivi de portefeuille DeFi et les outils de gestion de portefeuille traditionnels. La fonction d'optimisation est particulièrement intéressante - cela suggère qu'ils effectuent de réelles calculs de théorie de portefeuille (maximisation du ratio de Sharpe, analyse de la frontière efficace) sur vos avoirs en chaîne.

En gros : les métriques de risque TradFi rencontrent les portefeuilles crypto. À vérifier si vous gérez plusieurs positions et souhaitez des insights quantitatifs au-delà de "le chiffre monte".

The Cursor-xAI acquisition demonstrates technocapital's momentum in the IDE space. Cursor's AI-native code editor architecture—built on VSCode with custom LLM integrations for autocomplete, chat, and codebase-aware suggestions—attracted xAI's investment. This validates the commercial viability of AI-first developer tools that go beyond GitHub Copilot's scope.

Key technical implications:
• xAI gains direct access to millions of developer workflows and real-world coding patterns
• Cursor's inference optimization techniques (streaming completions, context window management) become xAI IP
• Potential integration of Grok models directly into the editor, competing with OpenAI/Anthropic partnerships

The deal signals consolidation in AI tooling—expect more acquisitions as foundation model companies vertically integrate into application layers where they can capture usage data and reduce API dependency costs.

Practical workflow automation pattern using Claude/GPT with MCP (Model Context Protocol) connectors:

1. Connect your tools via MCP servers, plugins, or API wrappers to Claude/Codex
2. Test cross-tool operations (e.g., "read Gmail → update Salesforce", "query CRM → send email")
3. Debug until the LLM executes reliably
4. Use skill-creator patterns to codify the workflow as a reusable prompt/function
5. Repeat for every repetitive task in your stack

Real outcome: You stop touching the underlying tools directly. CRM updates, expense reports, calendar coordination, JIRA tickets—all delegated to the LLM layer.

The bottleneck shifts from manual data entry to verification. You're trading synchronization overhead for occasional spot-checks.

This isn't theoretical—it's a concrete shift in how businesses can eliminate low-value cognitive load. The tedious glue work between systems becomes an LLM problem, not a human problem.

If you're not experimenting with MCP-style tool orchestration yet, start now. The ROI on automating your most-hated tasks is immediate.

Interesting thought experiment: What happens to Anthropic if local open-source models hit Opus 4.5 performance levels?

The technical gap is the moat. If open models reach parity on reasoning depth, context handling, and instruction following, the value prop of API-only access weakens dramatically. You'd get:

• Zero latency costs from network calls
• Full control over inference parameters and system prompts
• No rate limits or usage caps
• Complete data privacy (no external API calls)
• Ability to fine-tune on proprietary datasets

Anthropic's current advantages (safety alignment, reliability, support) matter less when you can run equivalent intelligence on local hardware. The economics shift hard when a one-time GPU investment beats ongoing API costs.

That said, reaching Opus-level performance locally requires serious compute. We're talking high-end consumer GPUs or multi-GPU setups for acceptable inference speeds. The real question: how long until open models close that 12-18 month capability lag?

DeepSeek, Qwen, and Llama are accelerating fast. If that gap shrinks to 6 months, the API business model faces existential pressure.

Opus 4.7 is showing unexpected common sense reasoning capabilities that weren't explicitly trained for. This is interesting from an emergent behavior perspective - the model appears to be making logical inferences and practical judgments that go beyond pattern matching in its training data.

This could indicate:
• Better world model representation in the latent space
• Improved chain-of-thought reasoning at inference time
• More effective alignment between pre-training and RLHF phases

Worth testing on standard common sense benchmarks like PIQA, HellaSwag, or WinoGrande to see if this translates to measurable improvements. If you're seeing this in production use cases, document the specific prompts - these edge cases often reveal architectural improvements that aren't obvious from standard evals.

Massive efficiency gain: chain your AI tools (Codex, Claude, etc.) to execute a workflow, then have them generate that workflow as a reusable skill.

Think of it as programmable hotkeys for complex job tasks. Instead of manually repeating multi-step processes, you're essentially creating custom automation primitives by having the AI observe and codify its own execution pattern.

The meta-loop here is powerful: AI assists with task → AI abstracts task into skill → skill becomes instantly replayable. Scales way better than traditional scripting because the AI handles the abstraction layer.

Real alpha is in the workflow composition - not just one-off prompts, but building a library of domain-specific skills that compound over time.

Exposure is fatal to your portfolio.

Unless you can see it.

→ https://raster.finance

Fu Peng (付鹏), ancien économiste en chef chez Northeast Securities, vient de rejoindre le secteur de la cryptomonnaie en tant qu'économiste en chef chez Huobi Tech basé à Hong Kong (maintenant renommé en Xinhuo Group).

Contexte sur Fu Peng : C'est un analyste macro bien connu dans la finance traditionnelle (TradFi), au même niveau que Ren Zeping et Hong Hao. Grande audience sur Bilibili.

Pourquoi ce changement ? Deux facteurs :
1. Les plafonds salariaux du secteur financier chinois ont eu un impact important—les institutions financières d'État limitent désormais les salaires des dirigeants à environ 2M RMB/an, avec des réductions en dessous. Les départements de recherche des sociétés de valeurs mobilières licencient des analystes, même les économistes en chef ne sont pas en sécurité.
2. Fu Peng a déjà quitté Northeast Securities en 2025 (officiellement pour des "raisons de santé"), il fait des médias indépendants depuis. Xinhuo a probablement fait une offre concurrentielle.

Ce que Xinhuo obtient : Ce n'est pas une question d'opérations de trading. C'est une question de positionnement de marque. Un analyste macro de TradFi donne de la crédibilité aux plateformes de cryptomonnaie licenciées lorsqu'elles s'adressent aux institutions. Fu Peng devient le "visage respectable" reliant la finance traditionnelle et la cryptomonnaie.

Cela compte parce que cela signale une tendance : des talents seniors de TradFi migrent vers des entités de cryptomonnaie licenciées. Fu Peng ne sera pas le dernier. À mesure que les cadres réglementaires se solidifient à Hong Kong et ailleurs, attendez-vous à ce que d'autres économistes et analystes de haut niveau fassent ce saut—surtout à mesure que les structures de rémunération de TradFi se resserrent et que l'infrastructure de la cryptomonnaie mûrit.

The doctor-patient dynamic is shifting hard. Patients now show up with AI-generated differential diagnoses, treatment comparisons, and research summaries from models like GPT-4, Claude, or specialized medical LLMs.

The technical gap: Most physicians aren't integrating AI tooling into their workflow. They're still operating on pattern recognition from residency + occasional journal skimming, while patients are running queries against models trained on PubMed, clinical trials databases, and medical textbooks.

What's breaking down:
- Information asymmetry (the doctor's traditional advantage) is collapsing
- Patients can now cross-reference symptoms against massive medical corpora in seconds
- Doctors who don't use AI assistance are getting outpaced on edge cases and rare conditions

The fix isn't just "doctors should use AI too" - it's about workflow integration. We need:
- Real-time clinical decision support systems (not just EHR alerts)
- AI-assisted differential diagnosis that doctors can interrogate
- Continuous learning pipelines that keep practitioners updated on latest research

The trust crisis is already starting. If your doctor can't explain why the AI's suggestion is wrong (or right), you're going to question their expertise. This is a tooling problem disguised as a social problem.

Token costs are dropping fast. @dokobot now offers unlimited free webpage scraping with no restrictions.

If token prices drop another 10x, deep research workflows become accessible to everyone — not just enterprises burning through API budgets.

We're talking about:
• Autonomous agents crawling and synthesizing multi-source data
• Real-time knowledge graphs built from live web content
• Context windows large enough to process entire documentation sites in one pass

The bottleneck isn't the models anymore. It's the infrastructure cost. Once that breaks, we'll see an explosion of research-grade AI tools in the hands of indie devs and students.

This is the unlock moment for democratized AI research.

X (Twitter) API pricing just got slashed by 90% for read operations starting tomorrow.

The technical reality: Musk realized that rate-limiting read access is fundamentally unenforceable. Too many workarounds exist - browser automation tools, scraping proxies, headless clients. The cat-and-mouse game wasn't worth the engineering overhead.

What this means for devs:
- Read API calls now economically viable for indie projects and research
- Data access barriers significantly lowered
- Expect surge in analytics tools, sentiment analysis bots, and monitoring services
- Write operations pricing likely unchanged (those actually cost server resources)

This is basically admitting that protecting public data behind paywalls doesn't work when the web is inherently readable. Smart pivot from a losing battle.

Cloudflare just dropped an AI Agent readiness scoring tool for websites.

This is basically a technical audit system that checks if your site's infrastructure can handle AI agent traffic patterns - think automated crawlers, API hammering, and bot interactions that differ from human browsing.

Key metrics it likely evaluates:
- Rate limiting configurations
- Bot management rules
- API endpoint resilience
- Response time under automated load
- CAPTCHA/verification mechanisms

Why this matters: As AI agents become the primary consumers of web content (not just humans), sites need different optimization strategies. Traditional anti-bot measures might block legitimate AI agents, while poorly configured systems could get overwhelmed by agent traffic.

Cloudflare positioning themselves as the infrastructure layer between websites and the incoming wave of autonomous AI agents makes total sense given their CDN/security stack.

This altcoin rally operates on completely different mechanics than previous cycles.

Traditional altcoin/meme bull markets follow a natural liquidity cascade: BTC pumps first → liquidity overflows → retail chases small caps. Simple contagion model.

This cycle? Pure market manipulation architecture.

Recent altcoin pumps are engineered through rapid 1-2 week accumulation phases by whales. Another cohort consists of legacy bags where whales achieved control distribution long ago, just waiting for optimal extraction windows.

Sweet spot: $20M-$100M market cap range. Why? Optimal liquidity control.

Core exploit mechanism = Price Oracle Control:

1. Whales accumulate spot until they own float
2. Mark price for perps = spot price on external exchanges
3. Whale controls spot price = whale controls liquidation triggers

The funding rate trap that most traders miss:

Funding rates aren't organic market signals here. After whale pumps spot, retail sees "obvious short setup" but lacks spot inventory → forced into perp shorts → one-sided positioning spikes funding rates negative.

Since liquidations use mark price (derived from whale-controlled spot), opening naked shorts = handing whales your liquidation trigger.

Triple extraction model:
- Profit on spot pump
- Liquidate shorts via price control
- Farm negative funding from short-heavy positioning

If you profited this cycle playing against this structure, you got lucky, not smart. The house always has architectural advantage when they control the price oracle.

Agents are becoming the new frontend, with websites relegated to backend infrastructure.

Google's search volume continues growing, but a significant portion is no longer initiated by humans.

This shift represents a fundamental architectural change in how systems interact:

• Traditional model: Human → Browser → Website
• Emerging model: Human → Agent → API/Website (as data source)

The implications are massive for developers:

Websites are transforming into API-first backends. Your beautifully crafted UI might never be seen by end users—only parsed by agents. This means:

- SEO is evolving into AEO (Agent Engine Optimization)
- Structured data and API quality matter more than visual design
- Rate limiting and bot detection strategies need complete rethinking

For search infrastructure specifically, non-human queries create new technical challenges:

- Query patterns differ drastically (agents batch requests, use different syntax)
- Caching strategies must adapt to programmatic access patterns
- Authentication and usage quotas need agent-specific tiers

The frontend-backend boundary is dissolving. If agents handle the interface layer, web developers need to think like API architects first, UI designers second. The web is becoming an invisible data layer beneath an agent-driven interaction model.

Opus 4.7 vs 4.6: No meaningful performance gains detected. The "xhigh" tier is completely unnecessary - tier 4 already maxes out the useful capability ceiling. Worse, users had to endure a noticeable quality degradation period during the rollout. Anthropic's deployment strategy here is questionable. Either their A/B testing framework is broken, or they're pushing incremental versions without proper validation. This reeks of version number inflation without actual architectural improvements.

Breaking down Jensen Huang's geopolitical chip strategy vs. the Dwarkesh counterargument:

Jensen's thesis:
• Model labs are fungible—talent flows bidirectionally between US/China, so OpenAI/Anthropic aren't structural moats
• Nvidia is currently irreplaceable, but Huawei will close the gap if given protected market access
• Export controls accelerate China's domestic chip R&D by forcing localization in a massive captive market
• China's actual advantage is energy infrastructure at scale—hence Jensen's push for US energy buildout
• Strategic play: Give China Nvidia access → they catch up on models but slow down on chip independence → buys time for US energy expansion while maintaining silicon lead

Dwarkesh's counter:
• US has already lost or will lose the energy production race
• Models are commodities (agrees with Jensen), so chips are the only leverage point
• Giving China current-gen Nvidia hardware could bootstrap their chip development velocity—they'd use those GPUs to accelerate their own silicon design

Core disagreement: Jensen bets Nvidia can use the same AI tools (or better) to stay ahead in the chip race. He's treating this as a compounding advantage problem where silicon leadership + energy scale = sustained dominance.

The meta-question: Is restricting chip access a time-buying strategy that backfires by forcing China into self-sufficiency, or does open access create a feedback loop where they leapfrog using your own tools? Jensen's betting on the former. Dwarkesh warns of the latter.

This is basically export control theory vs. market capture theory playing out in real-time semiconductor geopolitics.

Mythos is catching serious security vulnerabilities in smart contracts. The irony: billions still locked in DeFi protocols despite their track record of exploits.

Bitcoin's intentionally limited scripting language (non-Turing-complete) eliminates entire attack surface categories that Solidity contracts expose. No loops, no complex state machines, no reentrancy vectors.

The thesis: when the next wave of DeFi hacks hits (and statistically, they will), capital will rotate back to BTC. Not for yield farming promises, but for security guarantees through simplicity.

This isn't just about code audits anymore. It's about fundamental architectural trade-offs between programmability and attack resistance. DeFi chose expressiveness. Bitcoin chose constraints. The market might be about to reprice that decision.

Jensen Huang just dropped some spicy takes on China's chip ecosystem in his latest interview. Here's the technical breakdown:

Huawei AI Chip Assessment:
- Huang confirmed Huawei's AI chips are shipping at 1M+ units annually with solid performance metrics
- He's not dismissing them as vaporware—these are production-grade silicon at scale

China's Structural Advantages:
- Controls 60% of global mainstream chip production capacity
- Houses ~50% of the world's AI researchers
- Cheap energy + infrastructure = can compensate for per-chip performance gaps through horizontal scaling

The Distributed Computing Reality:
- AI workloads don't scale linearly with single-chip speed
- Total cluster throughput matters more than individual accelerator performance
- Current gen AI models (the ones dominating leaderboards) run fine on N-1 generation hardware when you add more nodes

NVIDIA's Real Moat (According to Jensen):
- Not the GPU silicon itself
- It's CUDA + the entire software ecosystem
- 50% of global AI devs write on NVIDIA's stack—that's the lock-in
- Export controls forcing China to build parallel toolchains could fracture this monopoly long-term

The Irony:
US export restrictions might be accelerating China's self-sufficiency rather than containing it. When you force a region with that much manufacturing capacity and engineering talent to go independent, you're potentially creating a competing standard.

TLDR: Huang basically said China can brute-force their way to competitive AI infrastructure even with older node chips, and trying to stop them might backfire by splitting the global dev ecosystem.

Après avoir tué cette vilaine bannière de débogage Chrome, Dokobot lit maintenant les pages web avec presque aucune interruption pour l'utilisateur.

Le seul indicateur visible ? Une petite icône animée sur la page.

Mise en œuvre propre - automatisation de navigateur qui ne crie pas "JE SUIS UN BOT" aux utilisateurs. C'est ce genre de finition UX qui sépare les outils prêts pour la production des preuves de concept.