I built a native DuckDB extension for TypeSafe Jev: vectorized input, batching, bounded concurrency, dedup and cross-query TTL/LRU caching. Live synthetic Choice run: 2,049 rows in 0.887s—2.3K rows/sec. Cached replay: 28.6ms, zero API calls. github.com/prasanthj/duckdb-…

I built a native DuckDB extension for TypeSafe Jev: vectorized input, batching, bounded concurrency, dedup and cross-query TTL/LRU caching. Live synthetic Choice run: 2,049 rows in 0.887s—2.3K rows/sec. Cached replay: 28.6ms, zero API calls. github.com/prasanthj/duckdb-…

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution

In a world with abundant intelligence, every enterprise runs on two kinds of capital: the human capital of its people's judgment, and the cognitive capital of the reasoning ability it builds and owns about the business. The second is new, and it is where durable advantage accrues. White-collar work in the enterprise forms a natural hierarchy. → At the base sit the *systems of record*: the warehouses, lakes, ERPs, and CRMs that hold the data. → Above it comes *knowledge work*: the assembly, reconciliation, and verification that turn scattered records into a trustworthy answer. It is procedural even when it's hard, and it's staffed today by armies of analysts and outsourced teams. → At the top comes *judgment work*: weighing trade-offs when systems disagree, owning the answer when challenged, the part that was always the key for world class decisions. Software increasingly absorbs the knowledge work. It still needs the judgment work. Cognitive capital accrues from both knowledge work and cognitive work. Today @isotopes_ai is introducing *Neocortex*, the self-learning system behind aidnn. Each answer it produces draws on a set of experts from your organization's verified knowledge, and shows you every expert who contributed and every belief it relied on. We built it the way the brain learns, by doing rather than by being instructed: → It holds what your organization believes → It learns how your organization does the work → It judges that work and learns from how you judge it → It consolidates every answer into durable cognitive capital A critical difference from existing approaches: Neocortex learns from doing the work, not from instructions you write down for every task. Every correction shows it how the work should be done, and the one thing it does better than any tool you've used is this: it never needs to be told twice. The verification layer within aidnn lets Neocortex *learn* the nuances that matter and, just as important, *unlearn* them as business realities change. The payoff: the reasoning your best people do today stops evaporating after each question and starts compounding into an asset you own. Interested in learning more? We are happy to show you Neocortex with *your* data: isotopes.ai/contact-us?sourc…

We just published our latest whitepaper "The Orchestration Imperative for AI Agents" and this one is especially interesting for leaders who want to learn about the next evolution of AI. Here's the core insight: Production-grade AI Agents aren't a chatbot problem. They are an orchestration problem. Most multi-agent AI systems fail for predictable reasons—coordination overhead scales nonlinearly, context gets contaminated and rots progressively, and debugging becomes impossible. We've watched teams spend months building "agentic" solutions only to hit these walls. This whitepaper distills the principles @isotopes_ai uses to make sophisticated AI agents work in production: structured workflows, plans as contracts, hierarchy that tames complexity, and humans owning the "what" while the system handles the "how", and more. If you're evaluating any agentic AI for your business - or building one - this is definitely worth a read! Get the whitepaper to learn more → isotopes.ai/whitepapers/orch…

Orchestrating a Team of Rivals We @isotopes_ai just published our in-depth technical paper on how aidnn achieves production-grade reliability! Model scaling is slowing down. But AI impact doesn't have to plateau. The path forward isn't faster clock-speeds. It's multi-core. Instead of waiting for a single smarter model, aidnn orchestrates multiple LLMs with opposing incentives. No business relies on one employee to handle critical operations. We shouldn't architect AI systems around single-agent execution either. aidnn deploys 50+ specialized agents organized as specialized teams: * Planners create execution strategies * Executors perform the work * Critics validate outputs with veto authority * Remote code execution keeps data isolated from reasoning models Each of these agents are coding agents resulting in a highly repeatable & reliable agentic system. We call it "orchestrating a team of rivals." Multiple agents with different failure modes catch what individual models miss. Hierarchical veto authority prevents errors from propagating. The result: 90%+ error prevention through layered validation. And your data stays secure. This is the architecture behind financial close automation at Invisible Technologies and others — turning weeks of manual work into hours of validated analysis. Read the full technical paper here: isotopes.ai/technology