... value the document does not print, which is the one thing this whole product exists in order not to do. One more, and it is a correction. In update #2 I wrote that values gives you "exactly the schema you asked for and nothing else". That was not true when the schema

... spent years writing the same CRUD boilerplate for every new backend, and I keep noticing the same pattern in the tools meant to fix it: scaffolders generate a project and abandon you the moment the schema changes, and low-code removes the boilerplate but locks the product into someone else's runtime. I've been working on a third way: a language-agnostic code generator. You describe the domain once in a small YAML model, and it produces real, readable ... ... quality of the generated code? And if "any language, you just write a bridge" is the model what language would you want first? Happy to dig into how it works under the hood the IR contract, deterministic rendering, schema

... years by people who cared about how it feels to use. Dark, considered interfaces. Whitespace that isn't accidental. Content that is the product, not decoration bolted onto a spreadsheet. The short answer to "what's the best LinkedIn alternative in 2026" is this: builders aren't looking for another feed. They're looking for a professional network that was actually designed, not just maintained. That's the gap forg.to is built to close. Here's why the gap exists ... ... down. So the platform where career reputations are supposedly built on visible expertise is now a place where the majority-vote experience is: is this even a person? R sum -Shaped Profiles Don't Fit Builder-Shaped Careers LinkedIn's schema

... finally read the whole study instead of the headline, and the actually useful findings are different from what got shared. First, in their realistic-config lane (instruction file + several MCP servers), the "light" harness came out HEAVIER: ~90.8k vs ~75k. A 72KB instruction file alone added ~20k tokens to every request, on both harnesses. Their own conclusion: configuration, not the harness, accounts for most of the production bill. The harness sets the floor, you set the ceiling. Second ... ... close to a fixed cost. An unstable small one can out-spend it. Size isn't the sin, churn is. Third, session shape flips the winner anyway. On a multi-step task the heavy harness finished cheaper (121k vs

... self-service if you already know how to model data." Then came text-to-SQL. The promise was that natural language would replace the query editor. In practice, a lot of these tools produced syntactically perfect SQL against a schema they didn't actually understand, confidently answering the wrong question because they guessed which table "revenue" lived in. Now we're in the agentic phase: systems that plan, retrieve context, run analysis, check their own work, and hand back ... ... modes that generalize to basically every tool in the category: Concept-to-entity ambiguity: a vague word like "revenue" or "active users" maps to many plausible tables, and the agent picks a reasonable-looking wrong one. 2. Data staleness: schemas

... gearing up to launch NexQL an AI-native PostgreSQL extension built entirely inside VS Code to replace bloated standalone clients and fragmented extensions. We've integrated everything from a fluid tree explorer and interactive SQL notebooks to visual schema

... Google still largely shows traditional results. AI search is mostly eating informational traffic, not purchase-intent traffic. Second, the traffic that AI does send is higher quality. ChatGPT-referred visitors spend an average of 15 minutes on a website versus Google-referred visitors at 8 minutes. Sessions from AI referrals grew 527% year over year in the first five months of 2025. Third, only 16% of brands are systematically tracking their performance in AI search. That means 84% of your ... ... competitors

... this just for that alone. curious how it decides what "stale state" looks like for a system it's never seen before though - is that something you configure per integration or does it infer it from the API schema? Comment from Gal Dayan(@galdayan): @rnagulapalle  "something you could trust, not something that guesses" is a good way to put it, and honestly the right call for v1. baking the precondition into the scenario spec rather than inferring it means ... ... test is deterministic and you can actually explain to someone why it failed, versus

... Schema Source paste any URL and it generates a ready-to-use JSON Schema

... Generative Engine Optimization (GEO) positions showed up in 14% of roles roles that simply didn't exist two years ago . The roles are evolving. The fundamentals are not. What job descriptions now ask for: Entity management over keyword targeting Schema markup as a brand asset Understanding how LLMs build memory and trust Optimizing for AI citations, not just blue-link ranking Agent-ready site architecture What they still ask for: Technical audits Content strategy aligned with intent GA4 and Search ... ... that's still being invented. We need to be honest. We need to learn. We need to experiment. And slowly, we will evolve. From SEOs to whatever shiny new title this world gives us . The future isn't human versus

... week and saw the breakdown: Category Tokens/Day (Before) Tokens/Day (After) Savings File reads (duplicates) 180K 12K 93% Tool outputs (JSON) 320K 64K 80% Build logs 95K 14K 85% Search results 150K 30K 80% Conversation history 200K 140K 30% Tool schemas 45K 8K 82% Total 990K 268K 73% Almost a million tokens per day, down to 268K. The quality gate ensures nothing important was lost. The Cost Breakdown Anthropic Claude Sonnet pricing: Input: $3 per million tokens Output: $15 per million ... ... file reads, fewer redundant tool calls, fewer wasted output tokens on re-doing work. Does Quality Actually Stay the Same? I was skeptical too. Here is what I measured over 4 weeks: Code that compiles first try: 78% (before) vs

... just relying on a human-in-the-loop to double-check everything? For example, are you using: Advanced RAG: Better chunking, hybrid search, or re-ranking? Self-Correction: Making the LLM verify its own sources against a strict schema? UI Guardrails: Forcing the system to visually highlight exactly where the answer came from? Drop your tech stack, your worst horror stories, or your current workarounds below. Comment from Saumil D(@saumild27): @alpertayfurr  Exactly. That s the part we care ... ... messy fast. The model can be on the right page and still read the wrong row, miss a not, or combine two facts that should never be joined. I like the pipeline you described: extract first, validate against a schema

... call "Zero-Input": 1. 100% Metadata-Driven Skeleton You can't have an AI Agent take over if your frontend and backend are hardcoded spaghetti. We stripped our entire system down to pure metadata. Every list, Kanban board, schema, and permission rule is abstracted into structured JSON. 2. Unifying Front-end and Back-end Actions We merged the definition of an "Action." When an action is defined in our system, the UI rendering, the server-side execution, and the workflow ... ... routing are all unified. 3. The Agent Reads the Skeleton, Not the UI Because the system is just metadata, our AI Agent doesn't need to "click" buttons or fill out web forms. It directly understands the database schema

... Confidence and overlap indicators on annotations. When the arrow is ambiguous (e.g. drawn between two close buttons in the screenshot), the JSON should signal that. Corey Clark asked on LinkedIn. Nested element hierarchy in the schema. Current screenshot schema

... Context Engine for my Models 4. Replicate - Content Generation & Training Image & Video Models 5. Resend - API managed emails Also, recently - its Klipio, My own tool for Static Image ad creatives from competitor ads. Comment from Justin Verlin(@justin_verlin): Claude Code VS

... explicit. If the system saw the whole prompt, say so. If it only saw metadata, say so. If it only saw the file diff, say so . If it saw nothing reliable, say that too. That sounds like a small schema choice, but it changes the whole conversation . Suddenly the record is queryable without pretending every tool exposes the same evidence surface. How are you handling partial AI capture today: one canonical schema

... structurally can't tell me an edit happened. So it's not only normalizing schemas, it's normalizing across different capability floors, and I don't want to design a schema

... upfront. GPT-4 tiles a 1024 1024 image into ~765 tokens before it reads your prompt. Send 10 images in a conversation, and your "cheap" chat call isn't cheap anymore. GPT-4o Realtime charges $100/1M audio input tokens vs $5/1M for text. Same model. 20 difference depending on how you send data. Batch API discounts give you 50% off on OpenAI, Anthropic, and Google but only if your architecture is deliberately designed around it. Most aren't. Agentic loops ... ... covering 97+ models across every AI modality: LLMs and multimodal (OpenAI, Anthropic, Google, Mistral, Meta, DeepSeek, xAI, Cohere, and more) STT per-minute rates, billing granularity, streaming support TTS per-character and per-audio-token pricing Realtime audio text vs

... Match-3", "Idle", and "Casual". Each activates different analysis strategies. Analysis Areas The patterns to look for like "Conversion Funnel", "Revenue & Pricing", or "Customer Retention". Prompts Markdown instructions that guide the AI through exploration, analysis, and recommendations. Profile Schema A form where users describe their business KPIs, business model, product catalog so the AI can tailor its analysis. DecisionBox ships with built-in packs for E-Commerce , Gaming , Social Network , and Real Estate CRM . But you can create your ... ... intervals Each area includes its own detailed analysis prompt. For example, the Conversion Funnel prompt tells the agent to check cart abandonment by price range, compare category-level conversion, and estimate revenue at risk and it defines what "critical" vs

... actually reason over. What it is One open-source layer for building health products on top of wearable data. Unified API across Oura, Garmin, Whoop, Apple Health, Polar, Suunto, Samsung, Strava, and Google Health Connect Standardized schema, so you stop writing per-vendor normalization code Open scoring algorithms you can audit, fork, and tune for your domain Coaching profiles for wellness, clinical, and performance use cases MCP server, so any LLM can reason over health trends instead of just reading ... ... last decade, we've shipped digital health, wellness, and clinical products with teams across regulated industries. And every single time, the same thing happened: weeks of plumbing before anyone touched the actual product. New vendor, new API, new schema

... threw out the HTML blob model entirely. Every page in NeoCMS is stored as a typed WidgetTree structured JSON where every widget has explicit props, validation rules, and container nesting logic. AI doesn't guess. It reads a schema, proposes changes through a tool-calling pipeline, and waits for your approval before anything goes live. Meet Aila your AI co-pilot Aila isn't just a page builder. She's a full CMS assistant: "Build me a pricing page with ... ... OpenAI, Gemini with your own API key. No vendor lock-in on the AI side. What's shipped so far This isn't a pitch deck. We've built and tested: 50+ widgets (13 live, 37+ in development) all schema

... massive datasets, they can be expensive, complex to query, and act as complete black boxes. If a search returns a weird chunk, diagnosing it is often a nightmare. Lately, I've been experimenting with a dead-simple alternative for smaller projects, and I'd love to know if anyone else is doing this: Dump raw data into a local folder. Use a strict markdown schema

... Google had 39 pages stuck in "Discovered not indexed." What I fixed: Switched profile pages from force-dynamic to ISR caching Converted my AI agent page from client to server component so crawlers can actually see it Added schema markup (BreadcrumbList, Person, Organization, SoftwareApplication) Removed unoptimized images, enabled AVIF/WebP Added an About page for E-E-A-T signals Updated sitemap with all missing pages Resubmitted the sitemap, checked Search Console, down to 2 not-indexed pages now. Ranking dipped ... ... hardest part of SEO is the patience. trying my best to get the first 100 users into my app :) Comment from ISHOLA OLUWASEYI DAVID(@neuralicai): This is solid work especially moving the AI agent page server-side and adding schema

... Last month, I did something that felt slightly insane. I took our product description, fed it into ChatGPT, and asked it to build a competitor. Not a parody. A real competitor

... building Verso ( projectoye.com ) A workspace OS that brings Notes, Spreadsheets, Presentations, Process Flows, and an AI-native IDE into one place. Today was a schema day . No UI. No demos. Just PostgreSQL and hard decisions. What I worked on Verso needs to support: Solo users Teams Enterprises all from the same codebase . That means the schema