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A validation failure is not an exception to hide. It is a record your system does not yet know how to trust. That distinction matters in LLM extraction pipelines. A malformed invoice, an unexpected OCR layout, a model response that violates the schema, and a semantically impossible value may all reach the same line of validation code. If the only outcomes are “retry” or “drop,” the pipeline will either waste money repeating the same failure or silently lose work. The production answer is a dead-letter path: a durable place for failed records to wait with enough evidence to explain, triage, and safely replay them. The queue itself is the easy part. The hard part is designing the failure contract around it. Validation is where routing begins Constrained decoding and post-hoc validation solve different problems . Even with both, some records should fail. Real documents are messy, schemas change, OCR corrupts values, and models sometimes return plausible nonsense. A robust validation boundary should produce more than true or false . It should emit a reason the rest of the pipeline can act on: which schema and model versions were used which fields failed and why whether the payload was malformed, incomplete, or semantically invalid the confidence signal attached to the extraction whether a retry is likely to change the result That result becomes a routing decision. High-confidence, valid records can flow forward. Recoverable transport failures can use a bounded retry. Ambiguous or invalid records belong in review or a dead-letter queue. Confidence-based routing is useful precisely because “trust everything” and “review everything” are both bad operating models. A dead-letter record needs evidence, not just payload Putting the original input on another queue is not enough. Without context, the team investigating the failure has to reconstruct the run from scattered logs—if those logs still exist. I would store a dead-letter envelope containing: a stable record ID and idem

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"When you multiply 0588235294117647 by 2, 3, 4, 5, …, 16, the same sequence of digits appears in a cyclic order. That is, the digits remain the same but start from a different position, and when they reach the end, they continue again from the beginning." Would be useful for hash tables? or something else that needs rotations/permutations? looks like a reversible data transformation what if instead of 16 values (4 bits), we get a bigger number for 65536 values (16 bits) ChatGPT says it's related to a particular property of 17 (a prime number), but I want to know the opinion of actual programmers, mathematicians and engineers. Thanks submitted by /u/digital_n01se_ [link] [留言]

Eae gente bonita, beleza? Depois de um longo período, resolvi voltar aqui e continuar compartilhando...

Local-first desktop agent for everyday work Discussion | Link

This is going to be a very different article from what I usually write. No technical discussions, architecture deep dives, or engineering practices today. Instead, we're talking about something much older than software itself: music. We treat language like it's the default mode of human communication, like it's the real and only thing used to communicate, everything else is secondary, emotional, aesthetic, nice to have. But language is actually the outlier. It's the new protocol layered on top of something much older. Music is the original standard and we've basically forgotten how to read it. The Protocol Stack Think of communication like a network stack. Language is high-level. It's TCP/IP. Built on assumptions, needs learning, breaks the second you cross a boundary. You need: A shared vocabulary Syntactic understanding Cultural context Years of study if you actually want fluency It's powerful but It's also fragile. And it's recent . Written language is a few thousand years old. Spoken language is older, sure, but both are late abstractions compared to the hundreds of thousands of years humans have been syncing bodies to shared sound. Relative to that timeline? Language is yesterday's patch. Music? That's the lower-level protocol. The physical layer everything else runs on. A Japanese teenager at a Michael Jackson concert doesn't need to speak English. She doesn't need to understand what "Man in the Mirror" means as a concept. She also doesn't need a music degree. Music isn't zero -cost. Genre, culture, convention still shape how we hear it. But the entry barrier for emotional communication is way lower. A rhythm can hit urgency, celebration, sadness, or tension long before anyone understands the formal structure behind it. Her nervous system speaks that fluently. And so does everyone else in that stadium. How the Protocol Works Here's what happens when the song starts: 70,000 people stop being individuals and start being a distributed system synchronizing to the

submitted by /u/Aires_id [link] [留言]

When an AI agent leaks data, it may not look like a breach at first. It may look like a normal tool call, a helpful API request, or a browser fetch that quietly sends the wrong payload to the wrong place. That is the uncomfortable part for builders: prompt safety can warn you about intent, but only the network boundary can stop bytes from leaving. If your product lets agents call APIs, browse pages, use MCP tools, fetch files, or run long workflows, you need a simple rule: agents should not have open internet access by default. They should pass through an egress proxy that can inspect, block, gate, and log every outbound action. Why this topic matters now Agent workflows are moving from demos into real development environments. Recent practitioner signals point in the same direction: CLI coding agents are becoming normal, MCP-style tool access is spreading, long-running agents need better harnesses, and teams are under pressure to prove AI ROI instead of just shipping impressive demos. That creates a new risk shape. Traditional backend code usually makes predictable network calls. You know the service, endpoint, payload shape, and permission model before deploy. AI agents are different. They choose tools at runtime. They read untrusted context. They may summarize a page, then call an API, then write to a ticket, then fetch a package, then retry with modified arguments. What is an AI agent egress proxy? An AI agent egress proxy is a controlled outbound layer between your agent runtime and the outside world. Instead of letting the agent process connect directly to any domain, the agent routes outbound traffic through the proxy. The proxy checks each request against policy before it leaves your environment. A minimal mental model: Agent runtime -> Egress proxy -> Approved external services | +-> policy checks +-> secret scanning +-> SSRF protection +-> approval gates +-> audit logs The proxy does not need to be magical. It needs to be boring in the best way: determinis

When building a website, choosing a button design can take more time than expected. You may want something simple, soft, colorful, dark, outlined, or slightly unusual—but comparing many styles usually means repeatedly editing CSS and refreshing the page. To make that process easier, I created a browser-based button showcase. Try It Online You can use it directly from the following page: https://uni928.github.io/Uni928PublicHTMLs/index78.html There is nothing to install. Open the page, browse the available designs, and choose a button you like. Many Button Styles in One Place The page includes a wide range of button designs, including: Light and subtle buttons Solid-color buttons Dark buttons Gradient buttons Outline buttons Rounded and pill-shaped buttons Buttons with icons More experimental designs The buttons are displayed as actual interactive elements, so you can compare their hover, focus, and pressed states directly in the browser. Click a Button to Copy It The main feature of this tool is its copy workflow. Clicking a button copies a minimal HTML example for that design. This makes it easier to take only the button you need instead of copying the entire showcase page. The generated example includes the necessary HTML and CSS, so it can be pasted into a new file and tested immediately. Copy Features for Faster Comparison The site also includes additional copy-related features to make browsing a large number of designs more convenient. You can: Copy a button directly by clicking it Review the generated code Copy frequently used button types from the quick-copy panel Receive visual feedback after a successful copy Use copied examples as standalone HTML files This is especially useful when you want to compare several designs before deciding which one to use in a project. Useful for Prototypes and Small Projects This tool is intended for situations where you need a usable button quickly, such as: Creating a prototype Building a small static website Testing a landi

The Challenge Rendering a complex 153-concept-node, 1,200-edge mythic relationship topology for mobile devices without triggering main-thread layout thrashing or heavy client-side JavaScript execution. The Technical Approach Build-Time D3 Force Layout Computation : Pre-computing node coordinates and physics simulation during the Astro static build step. Zero-Runtime SVG Pre-rendering : Outputting the rendered topology as inline SVG with CSS design tokens, preserving 60 FPS scrolling on mobile. Canonical Lore Data Architecture : Structuring 153 canonical concept nodes across Tang Dynasty exorcistic texts ( Nuo rituals) and Black Myth: Zhong Kui motifs. Check out the live interactive relationship map: Black Myth Lore & Concept Map Official website: blackmyth.game

If you've been anywhere near AI job postings lately, you've seen the title: Forward-Deployed Engineer. Sometimes it's "Deployment Engineer" or "Solutions Engineer" or "Applied AI Engineer." Sarvam is hiring more than a hundred of them. Palantir built a large part of its business on them. OpenAI, Anthropic, and a long tail of AI startups are all competing for the same people. And yet, if you ask five engineers what an FDE is , you'll get five different answers. Let's fix that. The one-sentence definition A Forward-Deployed Engineer is an engineer who is deployed forward — to the customer — to take a powerful but generic product and make it solve that specific customer's real problem. The word "forward" is borrowed from the military sense: you're not back at HQ, you're out in the field where the actual work happens. For an FDE, "the field" is the customer's environment — their data, their workflows, their constraints, their stakeholders. Think of it as part engineer, part consultant, part founder-in-the-field. You build, but you build for someone specific, sitting right next to you . Why AI companies need this role so badly right now Here's the thing about modern AI products: they're incredibly powerful and incredibly generic . A foundation model or an AI platform can do a thousand things — but an enterprise customer doesn't want a thousand things. They want their one problem solved, with their data, inside their systems, respecting their compliance rules. That gap — between "powerful generic product" and "solves my specific problem" — is exactly where deals are won and lost. And it's too custom, too messy, and too high-stakes to solve with documentation alone. So AI companies send an engineer to close the gap in person. That engineer is the FDE. One strong FDE can be the difference between a seven-figure enterprise contract signing or walking away. That's why the role is: High-leverage — your work directly moves revenue Well-paid — companies pay up for people who can

Serious engineering teams pay a quiet tax: dependency alerts nobody trusts, full test suites on one-line PRs, and CI YAML that only fails after a push. I built three small open-source CLIs that attack those loops: Tool Install Job vulntriage npm i -g vulntriage Which CVEs matter impactest npm i -g impactest Which tests to run gha-step npm i -g gha-step Run CI shell steps now npx vulntriage . --group --fail-on fix_now npx impactest -f vitest -q | xargs -r npx vitest run npx gha-step run test --dry-run All Apache-2.0. No SaaS. Designed to earn a place in CI by being boringly explainable. Canonical: https://sybilgambleyyu.github.io/posts/tool-family.html