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1. 

   8 Oct 2026 · 2 hr 2 minNew

   Steve Jobs in Exile

   The popular story of Steve Jobs is that he co-founded Apple, was pushed out, wandered through a failed venture called NeXT, then returned to save the company and usher in the iPod and iPhone. That version is memorable, widely repeated, and, in an important sense, wrong. His years at NeXT are usually treated as a footnote, when they were among the most consequential of his life and career. Geoffrey Cain is the author of Steve Jobs in Exile, which draws on previously unpublished material and new interviews to illuminate the NeXT years. The book argues that NeXT was not a failure but the hidden…

2. 

   6 Oct 2026 · 50 min

   Security in the Age of Instant Exploits

   In the world of software security, there was historically a comfortable lag between the moment a vulnerability became public and the moment attackers could exploit it. This lag was often measured in months, but AI models can now read a vulnerability report and generate a working exploit almost instantly. They can scan the entire internet for exposed secrets at a scale no human team could match, and surface complex flaws that traditional tools missed for years. However, the same capabilities are just as available to defenders, and there is a case to be made that defenders hold the stronger…

3. 

   29 Sep 2026 · 1 hr

   Cory Doctorow on AI, Work, and Power

   Cory Doctorow is a science fiction author, journalist, and technology activist, and a special advisor to the Electronic Frontier Foundation. He is the author of dozens of books, including the nonfiction Enshittification: Why Everything Suddenly Got Worse and What to Do About It and the novel Picks and Shovels. His work spans tech policy books like The Internet Con and Chokepoint Capitalism, the solarpunk novels Walkaway and The Lost Cause, and the bestselling YA Little Brother series. He holds honorary doctorates from York University and the Open University, writes the daily blog…

4. 

   24 Sep 2026 · 52 min

   Chroma and Agentic Retrieval

   Data retrieval is a fundamental challenge in AI systems, and the approaches for solving it are still evolving. Vector search was an early answer to the retrieval problem, but the rise of agentic systems has raised the stakes considerably. Agents issue queries at machine speed, decompose complex questions into parallel searches, and require retrieval infrastructure that can keep pace without becoming prohibitively expensive. Chroma is a company building open source infrastructure for AI applications, best known for its widely used database of the same name. The company also published the…

5. 

   22 Sep 2026 · 48 min

   Scaling Time-Series Workloads on Postgres

   Many real-world processes produce data as a continuous stream rather than as isolated records. Sensor readings, financial markets, and application telemetry all generate data this way. This kind of time-series data has a distinctive shape. It’s written far more often than it is updated, it accumulates continuously, and it is usually queried across ranges of time. Time-series databases are built specifically for this kind of workload. TimescaleDB is an open source database from Tiger Data that adds time-series capabilities to PostgreSQL. It’s implemented as a Postgres extension, so it…

6. 

   17 Sep 2026 · 51 min

   Scaling Agent Workloads at Vercel

   Most AI agent setups today are built around a single session, where one user interacts with one agent at a time. However, that model breaks down when an agent has to serve a business, where thousands of requests can arrive at once and each session needs to be isolated, durable, and recoverable. Getting agents to run reliably at that scale has meant a lot of hand-rolled infrastructure beneath the agent itself. eve is an open source, cloud-native agent framework from Vercel that removes much of the agent scaling burden. In the eve framework, an agent is defined declaratively through…

7. 

   15 Sep 2026 · 1 hr 18 min

   Inside Google’s Database Infrastructure for the AI Era

   Historically, databases were responsible for storing data and returning exact results in response to queries. However, AI is now bending that contract in a new direction. Applications increasingly expect structured and unstructured data to come together. This is pushing databases into territory that looks more like search, where relevance and ranking matter and results are no longer strictly exact. Agents are also beginning to write their own queries and even propose their own schemas, which raises new questions about how data should be structured, governed, and trusted. Sailesh…

8. 

   10 Sep 2026 · 51 min

   A Rust Framework to Simplify Distributed Systems

   A Rust Framework to Simplify Distributed Systems Building software that runs across many machines is notoriously difficult. Developers have to grapple with problems such as race conditions, partial failures, and message ordering. Notably, one category of distributed software has largely escaped these burdens. A distributed database can spread a single query across thousands of machines, handling the coordination, failure recovery, and ordering internally. This raises a natural question of why general-purpose distributed programming can’t feel the same way. This is a highly practical problem…

9. 

   8 Sep 2026 · 52 min

   SED News: The NVIDIA-Hugging Face Deal, China’s Proxy Economy, the Open Weight Surge

   SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer break down the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this end-of-summer episode, Gregor and Sean turn to a busy season of mergers and acquisitions, including NVIDIA ‘s reported $12.9 billion acquisition of Hugging Face , Dynatrace ‘s near-billion-dollar deal for AI observability platform Arize , and Temporal ‘s rumored raise at a $12 billion valuation. They also cover a run of large funding rounds, from AI security startup…

10. 

    3 Sep 2026 · 56 min

    Moving Beyond RAG with Precomputed Context

    Retrieval has become one of the central problems in building useful AI systems. The standard approach to grounding a model in one’s own data has been retrieval augmented generation, or RAG, where an agent searches a vector database for relevant information at query time. That pattern works, but it has limitations, such as retrieving information that’s not truly relevant, repeating the same lookup work on every query, and producing inconsistent answers to the same question. Pinecone is a vector database that’s widely used to power semantic search and RAG at scale. The team recently developed…

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