In episode 26 of Open Source Ready, Brian Douglas and John McBride sit down with Quinn Slack. They explore how Sourcegraph built Amp, why unconstrained coding agents represent a major shift, and why AI economics make traditional SaaS pricing impossible. Quinn also breaks down the rise of open source models, the future of developer tooling, and why ads might be the key to making AI accessible to everyone.

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Open Source Ready - Ep. #26, The Economics of AI Coding with Quinn Slack

In this debut episode of Data Renegades, CL Kao and Dori Wilson dive into the unconventional paths that led them into developer tools, civic tech, and machine learning systems. CL recounts his experience building early open source infrastructure, mobilizing communities through data transparency projects, and now shaping how we design software around LLMs. This episode sets the tone for the show’s mission: celebrating builders who redefine the boundaries of what tools can do.

Data Renegades - Ep. #1, Introducing Data Renegades

In episode 2 of Data Renegades, CL Kao and Dori Wilson speak with Simon Willison. Together they dive into the origins of Datasette, the evolution of data journalism, and the surprising ways open source tools shape global reporting. Simon also explains how LLM-based agents will redefine data cleaning, enrichment, and analysis. A must-listen for anyone building or scaling data teams.

Data Renegades - Ep. #2, Data Journalism Unleashed with Simon Willison

On episode 3 of Data Renegades, CL Kao and Dori Wilson sit down with Maxime Beauchemin. They explore the origins of Airflow and Superset, the evolution of open source in the data ecosystem, and how today’s tooling reshapes the role of the data practitioner. Max also shares a forward-looking perspective on agentic workflows and how AI is accelerating everything from BI to pipeline development.

Data Renegades - Ep. #3, Building Tools That Shape Data with Maxime Beauchemin

Medium Article: https://medium.com/@jsmith0475/when-all-your-ai-agents-are-wrong-together-c719ca9a7f74?postPublishedType=initial
"When All Your AI Agents Are Wrong Together," by Dr. Jerry A. Smith, discusses advanced architectures for achieving million-step reliability in Large Language Model (LLM) agents, building upon the foundational success of the existing MAKER system. Although MAKER demonstrates long-horizon stability using probabilistic voting, which relies on logarithmic cost scaling against exponential reliability, the article identifies three major flaws: vulnerability to correlated errors, the requirement for a fully explicit state representation, and high per-step costs. To address these limitations, the author proposes a new structure called TAC-HAVA-K, which incorporates adversarial reasoning (Thesis, Antithesis, Consolidator), hierarchical verification (Belief States, World Model, Verifier), and K-fold parallelism to create a more robust, cost-efficient, and generalizable system capable of operating in ambiguous, partially observed environments. Ultimately, the new architecture aims to achieve reliability through structural diversity of verification rather than relying solely on statistical independence.

When All Your AI Agents Are Wrong Together

Terapi yerine bir algoritmayla konuşmak… Erişilebilirliği artıran bu yeni yöntem, kimi kullanıcıları güçlendirirken kimi vakalarda tehlikeli yanıtlar verdiği iddiasıyla tartışma yaratıyor. Peki, yapay zekâ, duygusal destek için güvenilebilecek bir araç mı?

Psikolojik destek için milyonlarca insan yapay zekâya yöneliyor

Evaluating Small vs Large Language Models helps teams choose the right balance of speed, accuracy, and cost. Large models remain popular for their broad reasoning abilities, yet smaller ones excel when optimized for specific workflows. Through distillation and retrieval-based techniques, compact models can match or outperform larger counterparts in focused applications. https://www.clickittech.com/ai/small-vs-large-language-models

Small vs Large Language Models Insights

“Puzzled Expressions”, my old sonnet turned song. 57% faithful to the page, 100% worth a listen

Puzzled Expressions

Medium Article: https://medium.com/@jsmith0475/what-holds-an-ai-together-063fcb26c876
"What Holds an AI Together? The case for vertical causality in machine intelligence," by Dr. Jerry A. Smith, argues that contemporary artificial intelligence systems are fundamentally incomplete because they rely solely on horizontal causality, which governs the sequential flow of actions and feedback across time. This reliance on the temporal axis results in systems that are locally competent but lack global coherence, leading the author to introduce the concept of vertical causality. Vertical causality describes simultaneous, structural dependencies—such as the underlying architecture, goal representations, and identity models—that sustain the system and ground its purpose at every moment action occurs. The author explains that achieving genuine artificial agency requires integrating both dimensions in a "duplex ecosystem," where vertical structures define the space of possible behaviors while horizontal processes explore it. Consequently, robust AI alignment should focus not just on sequential checks but on the architecture itself, ensuring that essential commitments are structurally operative rather than merely procedural outcomes.

What Holds an AI Together?

Dan shares his thoughts on GOOGL, Mark Zuckerberg, the supposed AI bubble, the 2008 housing market collapse, and then, for some reason, agriculture. Come for the Um's, but stay for the Uh's!

080 GOOGL & AI Bubble Fears

Heavybit is the leading early-stage investor in enterprise infrastructure. Since 2013, we've helped launch and scale visionary technical startups—from DevSecOps and feature flagging to AI code generation and beyond. We go hands-on from day 0, backing founders as they turn code into companies and build the future of software from the bottom up to the top down.

In-Depth Explorations of Neuroscience-Inspired Architectures Revolutionizing AI.

Gündem veya önemli meseleler hakkında konunun uzmanlarınca kaleme alınmış makaleler Fikir Turu Podcast Kanalı'nda sizlerle

New tracks tagged #llm