By Hiral Shah, Senior Director, Product Management, Docusign A major recurring theme among the engineering teams at this week’s AI Engineer World’s Fair in San Francisco is the push to move specialized AI models out of research and directly into high-volume production. At Docusign, that optimization challenge happens at massive scale: we handle millions of transactions daily and have nearly 1.9 million customers in over 180 countries. Organizations have historically lost significant value every year to the friction, delays, and missed obligations that come from treating these agreements as static documents rather than live sources of business data. Much of that trapped value sits inside tables: the pricing schedules, SLA obligations, and contractor rate cards that define enterprise relationships but are often the hardest part of a contract to extract accurately. To solve this, we integrated NVIDIA Nemotron Parse , a vision-language model purpose-built for document understanding, directly into our document processing pipeline. Docusign and NVIDIA took the AI Engineer World’s Fair stage this week to give attendees a look at how the architecture works under the hood. Here’s what that looks like: Why Contract Tables Break General-Purpose AI Contracts routinely contain merged cells, multi-page structures, mixed formatting, and nested layouts that general-purpose vision language models (VLMs) and broad AI models weren't designed to handle. The result is inaccurate extractions that require manual correction, slowing down the workflows they are intended to accelerate. Our teams watch this operational friction play out across real enterprise scenarios every day: System Downtime: When a critical system goes down, operations teams need to know immediately which SLA notification requirements apply and to whom. Resource Tracking: When business stakeholders ask legal what hourly rate was agreed to in a contractor engagement, the answer is often buried deep inside a rate card tabl

In my previous article, I introduced the NeNe series: a family of small, self-hosted business tools for teams operating in Japan. Previous article: https://dev.to/hideyukimori/i-am-building-self-hosted-business-tools-for-small-teams-in-japan-4i26 This article is a deeper look at one of those tools: NeNe Records . Repository: https://github.com/hideyukiMORI/nene-records NeNe Records is an API-first typed CMS and flexible entity platform built on NENE2 , my small PHP framework for AI-readable business APIs. It is not trying to be a WordPress clone. It is not trying to run WordPress plugins or themes. The goal is different: manage business data and public content through typed schemas, documented APIs, and clear AI tool boundaries. Why not just use WordPress? WordPress is useful. It has proven that a generic content model can support blogs, pages, shops, media, and many small business workflows. But when I think about business data, a few problems keep coming back: untyped metadata plugin-specific data shapes hooks and filters that can change behavior from many places API contracts that depend heavily on plugins AI integrations that may not know where the real application boundary is The postmeta model is flexible, but it often becomes stringly typed storage. That is fine for many websites. But for business data, I usually want the API to know more: this field is text this field is an enum this field is an image this field is a relation this field is required this record belongs to this organization this operation requires this role That is the space where NeNe Records lives. Typed records instead of metadata chaos The core model is simple: Entity Type -> Field Definition -> Record An Entity Type defines what kind of data exists. Examples: posts pages products events internal documents A Field Definition describes the shape of a field: text markdown html blocks int enum bool datetime image file relation A Record is the actual data. The important part is that the schema

The latest Puppet Enterprise releases are out and this one has a huge load of improvements, fixes, and security patches included! Puppet Enterprise (PE) 2025.11 released! The full PE 2025.11 release notes are always the best way to get a full detail on what has changed, but here are some highlights of PE 2025.11! Certificate Authority (CA): Database-backed Storage This new optional feature adds support for storing CA data in a PostgreSQL database instead of the file system. This improves performance and reliability and introduces API-driven capabilities and enhanced backup and recovery handling. PostgreSQL 17 Supported PE-managed installations will automatically upgrade from verson 14 to 17 as part of the upgrade process, or you can update yourself before upgrading to PE 2025.11 Infra Assistant Goes GPT-5 GPT-5 series models are now running under the hood of Infra Assistant, improving the quality of responses and the consistency for queries. Advanced Patching Enhancements The advanced patching feature now has improvements across a variety of areas New puppet_run_concurrency setting allows you to get better performance out of patch group enrollment Improved validation of scheduled and immediate jobs to reduce risk of unintended or skipped executions. Cron scheduling has better user experience and improved validation across features. New configurable option to enable Puppet to run after patch jobs to refresh pe_patch facts New Endpoints for Classifier and Activity Service APIs The Classifier API introduced new tags , add-tags and remove-tags endpoints to manage node group tags. The Activity service API now has subscriptions endpoints to create subscriptions, list subscriptions, or fetch/delete a specific subscription. Agent Platform Updates, Resolved Issues, and Security Fixes The macOS 26 platform is now supported for both ARM and x86_64, while support has been removed for Ubuntu 18.04 and Ubuntu 20.04. Nearly 60 CVEs were addressed in this release, along with many r

by Sergey Karayev, cofounder @ Superconductor Recently, my wife and I sat down to plan an upcoming trip. Naturally, we each asked an AI. Trouble was, I had my chat and she had hers, and they knew nothing about each other. So we served as couriers between chatbots: her idea pasted into my chat, my hotel booking screenshotted into hers, the itinerary reconciled by hand in a Google Doc. I bring this up because your team probably works the same unfortunate way: each person in their own chat or coding agent session, with precious little shared. I've been building software with the same set of people for over a decade. In the past year, we all got a superpower: coding agents that can do extremely impressive things. But each one (Claude Code, Codex, Cursor, etc.) was built for a single player. That's fine and dandy if you're vibe-coding your own little app. It's just you and Claude, and it's absolutely magical. But put that same agent on a team and the magic fades quite a bit. The model is no longer the bottleneck. Coordination is. You don't know who's working on what. You can't see that an agent already tried the approach you're about to attempt, and abandoned it. You spend an hour re-deriving context that a teammate has, because it's trapped in their private chat. Now let me tell you of a better way. On the Superconductor team, every coding agent session is in the cloud, open to anyone else on the team to join. What this enabled was transformative. Code review improved first. My teammate reviews my work by joining the session I built it in. The session holds the full history of decisions, including the dead ends. Instead of Slacking me "why'd you name it this way?" she asks the agent. She gets her answer, and I never waste time answering. She also doesn't have to check out the branch locally — the live app preview in the cloud sandbox does the job. Handoffs became easy. If I have to pass a feature to a teammate, he picks it up with full context: what's done, what's left,

Digital Signatures: Format, Certificate, and Validation Policy Are Not the Same Thing The right move when a digital signature fails validation is don't look at the cryptography first . I know that sounds backwards. If the algorithm is RSA-2048 and the certificate chain is intact, why would validation fail? Because a signature can be cryptographically perfect and still get rejected by the validator. The problem isn't the hash or the private key — it's the format, the wrong certificate, or the validation policy the system is applying. My thesis is simple: most errors that look cryptographic in digital signatures are actually upper-layer errors — incompatible format, a certificate that doesn't meet the required profile, or a validation policy that the issuer and receiver never aligned on. And confusing those three layers has a real cost: debugging time wasted in the wrong place. The Real Mess: Three Layers People Keep Mixing Up When a digital signature fails validation, the typical mental sequence is: "Is the algorithm correct? Does the private key match the public one? Did the certificate expire?" Those are reasonable questions, but they're all in the same layer. The problem is there are three distinct layers, and each one can fail independently. Layer 1 — Signature Format The format defines how the signature is packaged together with the signed data. CMS/PKCS#7 is not the same as XAdES, PAdES, or JAdES. Each has variants: BASELINE-B , BASELINE-T , BASELINE-LT , BASELINE-LTA . Choosing CAdES-BASELINE-B when the receiver expects XAdES-BASELINE-LT produces a rejection that has nothing to do with the cryptographic algorithm. The public documentation for DSS (Digital Signature Service) from the European Commission describes these variants in detail. DSS is the reference library for eIDAS-compliant signatures, and its documentation is one of the most complete and verifiable resources publicly available. Layer 2 — Certificate The certificate is the signer's identity, but it

When the enemy is in distress, exploit the opportunity to seize advantage. — The 36 Stratagems, Loot a Burning House Who's Leo — In the last story , he was CoreStack's backend lead — the guy who built the core system alone over five years with zero P0 incidents. Then a new CTO named James showed up, spent $8M on his old employer's product, and laid off Leo's entire team. Thirteen days later, that $8M AI system collapsed — three agents fighting over context, OOM taking down six GPU servers, a 37% order duplication rate, and 2,300 customer complaints. Leo pulled the old system off his laptop, flipped one line of Nginx config, and restored service in thirty seconds. The CEO called him at 3 AM begging him to come back. He came back. Three conditions: kill the paid AI product, AI assists only — never touches the primary pipeline — and engineers decide the architecture, not the guy writing checks. The CEO agreed to all of it. So who's Leo now: CoreStack's CTO. Technically confident to the point of arrogance. Zero talent for upward management. No idea how many people he pissed off on the board with those conditions. Doesn't care. He only knows one thing — the system he built is still running. That's all the proof he needs. Then a Slack message cut him off. The Signal 12:47 AM. CoreStack's CTO gets a Slack notification. The account has no profile picture, no display name, no status. Account creation timestamp at the bottom — 00:43. Four minutes old. Seven characters: Check CodeForge's status page. Leo taps it open. CodeForge's status page is all red. Payment Routing — Major Outage. Investigating. All customers affected. Status has been active for approximately 3 hours. He pulls up CoreStack's CRM. The sales team's prospect list has ShopStream at #2 — a potential whale, with "Current Provider" reading CodeForge. E-commerce platform doing 470,000 transactions a day . An hour of downtime costs them $210,000 . If this drags on until morning? He doesn't want to do the math. Core

Early-stage firm Magnify Ventures has raised a $46.6 million Fund II from LPs including Melinda French Gates’ Pivotal Ventures

OpenAI CEO Sam Altman has reportedly proposed giving 5% of the company’s equity to a U.S. sovereign wealth fund, reviving discussions about letting the public share in the financial gains from the AI boom.

TV Time, the popular TV-tracking app, is shutting down on July 15 as parent company Whip Media pivots toward enterprise AI products.

Insiders say Sam Altman is in active talks with the Trump administration.

Hi HN, we are Pietro and Luigi, cofounders of Manufact ( https://manufact.com ), a cloud for MCP apps and servers. We used to be called mcp-use, and still build open source SDKs for MCP under that name: https://github.com/mcp-use/mcp-use . We did a Show HN about that last year: https://news.ycombinator.com/item?id=44747229 . Today we want to tell you about our cloud product, Manufact, which is to mcp-use as Vercel is to Next.js. Manufact is an MCP vertical cloud designed for dev teams putting MC

If you don’t want to spend $180 on the Elgato Key Light, the Godox ES45 Desktop LED Key Light is a more affordable option that offers nearly as much brightness and plenty of features, including adjustable brightness and color temperature. Right now, it’s cheaper than usual for the first time this year, selling for $119 […]

The company announced a new slate of executive hires meant to help turn things around, as Gravity SUV sales are not taking off as expected.

Together with a friend, we were developing a golf application. Our codebase grew rapidly and became split between multiple repositories: the iOS app, Android app, backend, front-end, and extra tooling. Both of us also work in larger scale-ups, and we saw the same problem: understanding large distributed codebases becomes progressively harder. Yay for microservices. It takes time to understand and answer questions like: - What calls this function? - What is the impact of changing this interface?

Capcom is trying to get Onimusha: Way of the Sword out the door before the fall release calendar gets really crowded.