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What I actually found when I stopped reading about AI and started running my own experiments. Everywhere you turn right now, someone is telling you how AI is going to transform your workflow, your team, your organization, your life. The content is relentless, and it is almost universally positive. Glowing. Evangelical, even. I'm not here to tell you that's all a lie. I genuinely don't know. That's kind of the problem. We live in a media environment where the line between advertising...

Filament's export action is great. It's quick to set up, supports queued exports, includes column mapping, handles notifications, and keeps a history of generated files through the Export model. For most use cases, it's exactly what you need. But I recently ran into a limitation that the native export couldn't solve. When XLSX isn't really Excel I was exporting financial data or measurements from a Filament table. The export worked. The file downloaded. Excel opened it witho...

Rate Limiting and Circuit Breakers in Distributed AI Systems Distributed AI systems are inherently complex, handling massive volumes of requests, variable latency from model inference, and dependencies on external services like GPU clusters, databases, or third-party APIs. Without proper safeguards, a single misbehaving component or a sudden traffic surge can cascade into system-wide failure. Two fundamental patterns— rate limiting and circuit breakers —provide essential protection. This p...

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Lightly edited transcript of Richard Hipp, creator of SQLite, from 28'53": Suppose you had a pull request for SQLite. "Hey, I've got this new feature for SQLite. Here's the pull request." When you want me to pull that into the tree, you say, "Oh, it's free." No, it's not free. What you're doing is asking me - you've got this cool feature, and you want me to maintain it for you, to document it for you, to test it for you, to maintain it for you for the next twenty-five years. That's not fre...

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Crypto's worst month ever lost $630M, almost all through access-control failures, not code bugs. Three risk tools sell private analysis to paying clients; only CORE3's open Probability of Loss score rates every protocol's key-topology risk publicly, the way CCSS and proof-of-reserves did before it.

AI's real problem isn't model quality. The real issue is that the pipeline connecting data to decisions is unverifiable, centralized, and owned by someone other than you. A trustworthy stack separates data, compute, model, and application into distinct layers, each closing a different gap in the accountability chain. The protocols to build this already exist: permanent decentralized storage, reproducible inference environments, signed model outputs, and permissioned data contracts. By 2030, the ...

OpenWeightHub proposes a permanent, verifiable registry for open AI model weights, datasets, licences, and release metadata.

Data platforms are evolving into AI-native operating systems where Agents, Context, and Workflows become the new interface to enterprise intelligence.

Local LLM servers are great at generating tokens, but modern clients expect more than inference: state, lifecycle endpoints, streaming shape, tool protocol, files, and metrics. Respawn is an open-source gateway that sits in front of Ollama/self-hosted backends and adds OpenAI Responses API semantics locally.

BrainMate gives AI teams shared memory, observability, and governance across tools so every AI action is remembered, auditable, and controlled.

Learn how capital friction, regulatory barriers, taxes, and jurisdictional risks can trap wealth despite strong investment performance.

Claude Code is not the only coding assistant out there. Many of these options are superior for some use cases. Some, like Google AI Studio, are free or have generous free tiers. Here are the top 10 best alternatives to Claude Code today!

AI agents have made writing code nearly free, but this has introduced a new, hidden cost. The most expensive bugs are now caused by what the agent didn't write—missing edge case handling, like ensuring a money transfer is atomic (succeeds or fails completely). Our current tools (tests, code review) are good at finding errors in existing code but bad at spotting what's absent. This shifts the bottleneck from writing code to the slow, manual process of expert human review, as engineers must meticu...

June 2026 shows how AI conferences are shaping sovereign AI, agentic AI, infrastructure policy, standards, and responsible deployment.

A 3D agent has to do more than produce geometry from text or images. It has to help the user move through the unclear space between an idea and an asset they can use.

Training a frontier AI model means convincing hundreds of thousands of chips to act like one giant computer. The hard part isn't the chips — it's the wiring.

For years, crypto has framed centralized exchanges (CEXs) and decentralized finance (DeFi) as competing visions of the future. The reality is that both suffer from the same core problem: capital fragmentation. Traders often have assets spread across exchanges, blockchains, wallets, and yield protocols, making it difficult to deploy funds quickly when opportunities arise. While CEXs offer fast execution and DeFi offers transparency and ownership, neither solves the challenge of making capital in...

Protocol gaps between Modbus and OPC-UA break industrial AI pipelines. Learn what sits between them and how to get the conversion right.

Higher rankings don't always mean more customers anymore. As Google makes it easier for users to call and hire businesses directly from search results, traditional local SEO metrics are becoming less reliable indicators of customer acquisition. Many businesses track visibility but fail to track where leads actually come from. The real advantage now comes from understanding attribution, customer behavior, and which channels generate revenue.

If general intelligence is on the horizon, simply making one model larger won’t get us there. We need to create networks of specialized models that can communicate, debate, and share their knowledge.

Most teams track AI visibility using made-up prompts. This article shows a 6-step process to build a prompt set from real buyer query data (Search Console, Google Ads, Bing), score and convert them into conversational prompts, and track visibility as probability rates — not ranks.

The Swiss Deep Tech Report 2026 ranks Switzerland first in the world for the share of venture capital going to deep tech, at 63%, ahead of China (56%) and the United States (54%). At $1,470 per capita, Switzerland invests more in deep tech per head than any country in Europe, ahead of Sweden ($1,328), and ranks top three worldwide. Annual funding has climbed from $543m in 2015 to a record $2.6B in 2025, the best year on record. AI and machine learning now account for one in four new Swiss dee...

Most teams design the historian first and connect it to a Unified Namespace later. This article argues the reverse: the UNS owns identity, so it should dictate the historian schema. That means storing tag identity in a relational namespace table with surrogate keys and keeping history in a narrow hypertable. The result: tag renames, hierarchy changes, and churn happen without rewriting history.

Most finance creators aren't traders; they're marketers who learned that trading content converts. The business model runs on broker affiliate deals, courses, and subscriptions that pay out regardless of whether you make money. Here's how to spot the playbook before it costs you.

It’s like Big Brother without any limits, or broadcast standards. WIRED goes on location—and on camera—with the cult hit.