Author: Carter

way to build enterprise RAG, and it disagrees with a lot of standard practice along the way. RAG is not machine learning; embeddings are not magic; a chunk-size sweep optimizes the wrong thing; the answer schema matters more than the model. None of these are neutral, and each one changes what you build. This piece collects the positions the rest of the series argues from, then maps the series article by article, so you can go straight to the argument you want to check. This article is a manifesto of Enterprise Document Intelligence, a series that builds an enterprise RAG…

through prompts. We can describe the task, give instructions, and tell Codex what kind of result we expect. This allows us to control how the agent approaches its work. But sometimes, prompting is not enough. We may want to further customize the execution by running our own logic at different stages of a Codex session. So, how can we do that? The answer is Codex hooks. In this post, we’ll explore the concept of hooks and understand where they fit into the agentic loop. Then, we’ll go through a concrete case study to demonstrate the concept. 1. Understanding Codex hooks…

Posted on August 24, 2026 by Santosh Balaranganathan, Michael Yoo, James Moessis, James Kieltyka, Jason Lee, Lavender Neesham – Atlassian CNCF projects highlighted in this post The problem: Humans shouldn’t be correlation engines At Atlassian’s scale, hundreds of interconnected microservices distributed across multiple regions mean a production incident generates an overwhelming volume of telemetry. The problem is that finding the causal factor in the vast amount of telemetry still relies heavily on human expertise, intuition, and manual cross-referencing. A typical root cause analysis workflow today looks something like this: an on-call engineer gets paged, opens a metrics dashboard, spots an…

Industry surveys have long put CISO tenure below that of other C-suite roles, and part of the reason is a double standard. During recruitment, the focus is technical depth, security experience, and leadership. But when budget season arrives and the board weighs a leader’s performance, the lens is cost, growth, customer trust, and brand protection. Many CISOs feel this acutely. They came up through security, or through risk and compliance, and that is where they are fluent. Their board is not. It wakes up thinking about cost, growth, and customer commitments, and a security leader who cannot connect their work…

Bitget CEO Gracy Chen isn’t convinced that Bitcoin’s recent rally to $79,000 marks the end of the bear market.She is keeping a significant portion of her portfolio in stablecoins and watching for a drop of more than $25,000 from current prices as a potential level to buy back into Bitcoin. “I would say $50K is the number that I’m looking at for my personal Bitcoin buyback sort of price,” Chen tells the Trade Secrets show.That being said, Chen isn’t claiming to have any special insight into where Bitcoin’s notoriously unpredictable price is headed. She also thinks its possible Bitcoin could…

The biggest challenge facing retailers today isn’t a lack of technology, it’s making existing systems work together. Here’s why integration and edge intelligence are becoming retail’s next competitive advantage. Retailers have never had access to more technology. RFID readers, smart sensors, self-checkout platforms, electronic article surveillance systems, point-of-sale infrastructure—the list keeps growing. And yet, for many store operators, more technology hasn’t meant more clarity, it’s meant more complexity. The uncomfortable truth the retail industry confronts is this: the bottleneck is a failure to integrate what already exists. Walk through the back end of a modern retail operation and you’ll find…

In the Wild What is moving before it becomes a press release. See the full pulse in In the Wild. Harness optimization reached 18,600 Telegram views. Scale AI’s new benchmark tests whether one AI can improve the prompts, tools, memory and control flow around another. The paper found that the optimizer model mattered more than the coding harness it used. Read HarnessOpt-Bench. Conduit climbed five places to No. 11 in Productivity. The iPhone client connects to a user’s own Open WebUI server, a small sign that self-hosted AI is reaching beyond desktop tinkerers. See Conduit. Vidmor rose three places to…

Grok 4.6 is xAI’s latest frontier model, built specifically for coding, agentic tasks, and knowledge work. It focuses heavily on long-running agents that can work across a codebase, research problems, test their work, and keep going through complex multi-step tasks. According to xAI’s evaluations, Grok 4.6 reaches frontier-level performance and matches GPT-5.6 Sol on the Artificial Analysis Intelligence Index. What makes it even more interesting for developers is Grok Build, xAI’s own terminal coding agent and TUI. Instead of relying on a third-party coding interface, xAI built a full-screen, interactive terminal experience specifically for working with its models, and Grok…

By Marc Kavinsky, Lead Editor at IoT Business News.Omdia is forecasting satellite IoT connections to reach 197.6 million by 2035, underscoring the growing role of non-terrestrial connectivity in the wider IoT market.For many IoT deployments, the hardest connectivity problem is not bandwidth. It is reach. Assets, meters, sensors and industrial equipment often operate beyond the footprint of terrestrial networks, particularly in remote logistics corridors, offshore environments, agricultural areas and sparsely populated regions.That context gives weight to a new Omdia forecast indicating that satellite IoT connections are expected to reach 197.6 million by 2035. The figure points to a market moving…

THEMES = { “BlackOnWhite”: dict(bg=”#ffffff”, fg=”#000000″, grid=”#c8c8c8″, cycle=[“#3465a4”, “#cc0000”, “#4e9a06”, “#f57900”, “#75507b”, “#06989a”]), “Dracula”: dict(bg=”#282a36″, fg=”#f8f8f2″, grid=”#44475a”, cycle=[“#8be9fd”, “#ff79c6”, “#50fa7b”, “#ffb86c”, “#bd93f9”, “#f1fa8c”]), “SolarizedDark”: dict(bg=”#002b36″, fg=”#93a1a1″, grid=”#0f4b57″, cycle=[“#268bd2”, “#dc322f”, “#859900”, “#b58900”, “#6c71c4”, “#2aa198″])} class XYCurve(AbstractAspect): def __init__(self, name, x=None, y=None, lineStyle=”-“, lineWidth=1.6, symbolStyle=None, symbolSize=4., color=None, alpha=1., zorder=2): super().__init__(name) self.xColumn, self.yColumn, self.color, self.alpha = x, y, color, alpha self.lineStyle, self.lineWidth = lineStyle, lineWidth self.symbolStyle, self.symbolSize, self.zorder = symbolStyle, symbolSize, zorder self.yErrorColumn = self.fillBetween = None def setXColumn(self, c): self.xColumn = c; return self def setYColumn(self, c): self.yColumn = c; return self @staticmethod def _v(c): return c.values() if isinstance(c, Column) else np.asarray(c,…