BuildShip

BuildShip AI

BuildShip AI

BuildShip AI

BuildShip AI

BuildShip AI

BuildShip AI

BuildShip AI

BuildShip AI

BuildShip AI

BuildShip AI

Would you believe I built this in a single shot with Fable 5 ?

Hey folks 👋 Been building Linkwise (an AI read-later / knowledge app) and just shipped a feature called Discover - a curated feed of articles, essays, videos and highlights I actually find worth reading. It's a public, no-login page: linkwise.app/discover Here's the project and here's how I made it: Stack Next.js with ISR, so the pages render static and stay SEO-friendly Supabase / Postgres for the content Fable 5 to generate the page The "single shot" part Instead of hand-building the page, I gave Fable 5 the full context up front: my Postgres schema using supabase connector, the shape of the data coming back, and my existing design tokens/components so it'd match the rest of the app. One prompt, and it wrote the entire /discover route, the server-side data fetch, the ISR config, and the grid layout for mixed content types (articles vs. videos vs. highlights). What actually made the one-shot work (the useful bit): Feed it the schema first. The moment it had the real column names and types, the data mapping came back correct instead of hallucinated. This was the single biggest lever. Give it your design system, not just "make it look nice." Passing my existing components/tokens meant the output dropped straight into the app without a restyle pass. Gotcha: it defaulted to client-side rendering. I had to explicitly steer it toward ISR / static rendering, since that's the whole point for an SEO page - worth stating in the prompt rather than fixing after. Total edits after generation were minor - mostly wiring it to live data and a bit of spacing. Would love feedback on the feature itself. And if you've got something worth curating, drop it in the comments or mail me at [dheeraj@linkwise.app](mailto:dheeraj@linkwise.app) 🙏 submitted by /u/dheeraj_iosdev [link] [comments]

Weekly recap: GPT-5.6 public launch, Grok 4.5, Gemini 3.5 Pro delayed, Microsoft Copilot conversion data, DeepSeek API retirement on July 24

Big week, so a consolidated rundown for anyone catching up. OpenAI released the GPT-5.6 family publicly on July 9 after a limited partner preview — Sol (frontier reasoning), Terra (previous-flagship performance at ~2x lower cost), Luna (fast/cheap). They also shipped GPT-Live-1, a full-duplex voice model that handles simultaneous listening/speaking, plus gpt-realtime-2.1 with ~25% lower p95 latency. xAI launched Grok 4.5 (trained alongside Cursor) at $2/M input and $6/M output, claiming Opus-class performance on coding/legal/finance tasks. Independent evals aren't in yet, so treat the claims accordingly. Google delayed Gemini 3.5 Pro to July 17 — full architectural rebuild, 2M context. Separately, four senior DeepMind researchers departed in one week (Shazeer to OpenAI; Jumper, Adler, Pritzel to Anthropic), and Alphabet dropped ~$225B in market cap. Microsoft is merging its Copilot apps into one by August. The notable disclosure: fewer than 4.5% of 450M M365 seats have converted to paid Copilot. Meta launched Muse Image, its first Superintelligence Labs model — agentic image gen that invokes search/code tools and self-refines. Trains on public Instagram photos by default (opt-out). Open source: Ollama raised $65M Series B (8.9M monthly devs). Gemma 4 got ~90% faster on Apple Silicon in Ollama via multi-token prediction. And a PSA — DeepSeek retires deepseek-chat and deepseek-reasoner on July 24. One-line migration, but note deepseek-reasoner maps to v4-flash thinking mode, not v4-pro, so heavy reasoning workloads should evaluate v4-pro explicitly rather than trusting the alias. My take as someone building on top of these APIs: the simultaneous price drops (Terra, Grok 4.5, Sonnet 5's intro pricing) matter more than any single benchmark. Near-frontier inference costs fell across four vendors in one week, which changes what's economically viable to automate. Meanwhile Microsoft's 4.5% suggests horizontal assistants aren't converting even with unlimited distribution — the demand seems to be for task-specific automation, which matches what I see with SMB clients. And the DeepSeek cutoff is a good reminder to abstract your model layer. Sources: OpenAI/xAI/Meta blogs, Euronews, Bloomberg, TechCrunch, CNBC, TechTimes coverage this week. submitted by /u/ksraj1001 [link] [comments]

i'm 16 and was drowning in junior year. vibecoding is the only reason i got a real app onto the app store

a few years ago, me shipping an ios app during junior year would've been a joke. i'd have needed a year just to learn swift, and i've got maybe an hour before school and whatever's left after homework. vibecoding flipped the bottleneck from "do you know the language" to "do you have a clear idea." i built my whole first app in the margins of my day, one small piece at a time, with claude code doing the syntax while i made the calls on what it should be. not magic though. lazy prompts got me spaghetti, and i had to learn real discipline to ship (spec first, revert instead of patching, test on device). i learned engineering by shipping, not before it. think it's ai slop? fair, i'd be skeptical too. especially cause im in high school. but judge it yourself here. five AIs debate your hard decision into one verdict, free to start. real question for other students here: what would you build if the "i can't code" wall was gone? because it is!! and i don't think enough of us understand that right now. submitted by /u/wartableapp [link] [comments]

Devs shipping AI agents what does your security testing look like ?

Building security testing tools for AI agents for the past few months and realised teams build the agent then test it for accuracy and test it for hallucinations. Do you test for prompt injection, system prompt extraction, data exfiltration until it breaks in production. I used to think the LLM's model is smart enough to handle it and that was my initial security plan. What are your experiences and Do you test for malicious inputs before shipping? If yes whats does that process? If no what would make you start? submitted by /u/Still_Piglet9217 [link] [comments]

after months of building, i shipped my first ever iOS app today!!

kept using AI for actual decisions, not "write my email" but real ones like whether to take a contract or an idea worth building, and i realized the answer just depended on which model i happened to open. one says go, one says wait, one hedges. i wasn't getting an answer, i was getting one model's opinion in a confident voice and treating it like it settled things. so i built the opposite. you give it one hard decision and five different models (claude, gpt-5, gemini, grok, qwen) each argue it from a locked role across three rounds, then you get one verdict with the disagreements kept visible instead of smoothed into a safe average. the disagreement turned out to be the actual signal, the one model that broke from the pack was usually pointing at the thing i'd skipped. it went live on the App Store this morning, which still feels unreal. free to start: https://apps.apple.com/us/app/war-table-ai-council/id6780293764 genuinely curious what people here think though, do you trust the disagreement between models more than the consensus, or is that just reading signal into noise? submitted by /u/wartableapp [link] [comments]

ORBIS - Daily Briefing

https://orbis.aurochthryx.com submitted by /u/CarterBirchll [link] [comments]

This week in AI: GPT-5.6, Gemini 3.5 Flash, Claude Science, and a Qwen price war — inference cost is collapsing across every tier at once

Lot dropped this week and there's a pretty clear through-line, so figured I'd pull it together. Model releases: - OpenAI launched GPT-5.6 (Sol/Terra/Luna). The bit worth noting isn't the flagship — it's Terra, reportedly matching GPT-5.5 quality at ~2x cheaper, with Luna aimed at the low-cost end. - Google shipped Gemini 3.5 Flash (beats 3.1 Pro on several benchmarks), plus Nano Banana 2 Lite (images ~$0.034/1K-res) and Gemini Omni Flash (video ~$0.10/sec via API). - xAI made Grok 3 GA and Grok 4.1 live for everyone. Grok 5 still hasn't shipped, which is its own story at this point. Vertical / enterprise: - Anthropic launched Claude Science for pharma and lab research. Separately, the US govt lifted the export restrictions on Fable 5 / Mythos 5 that it had imposed only weeks earlier. - Mistral shipped OCR 4 (on-prem, structure-aware extraction) and is reportedly raising ~€3B at ~€20B. Open source: - Ollama crossed 52M monthly downloads, added `ollama launch` (one command to run coding agents on local or cloud models), and is now compatible with the Anthropic Messages API. - Hugging Face: agents can train models via Hub skills now; Meta + HF also launched OpenEnv for agent environments. Funding: - Together AI raised $800M Series C (~$8.3B post). Crunchbase notes ~88% of 2026 AI funding went to US companies. My take as someone building on top of these APIs: The thing I keep noticing is that the price collapse is happening across every tier simultaneously, not just at the bottom. When the "balanced" model gets 2x cheaper each generation and the Flash tier beats last year's Pro, it gets really hard to build a business whose only edge is "we use the best model." That edge evaporates on someone else's release schedule. The stuff that looked durable this week was all workflow-and-data — Claude Science, Mistral's on-prem OCR, Alibaba's agent ecosystem. Would genuinely like to hear how others here are handling multi-provider abstraction, because a surprise price or availability change shouldn't be able to wreck your margins overnight. And the frozen-then-unfrozen Anthropic thing means model availability is now a supply-chain risk, not a hypothetical. submitted by /u/ksraj1001 [link] [comments]

AI didn’t replace the work for me. It moved the stress to a different place.

I don’t feel like AI has made work “effortless.” It has mostly changed which part of the work feels hard. Before, the hard part was usually getting a first version done. Writing the first draft, building the first page, outlining the first plan, or turning a rough idea into something real enough to look at. Now that part is much faster. But I notice the stress moved somewhere else. Now I spend more energy asking: is this actually correct? did it miss the weird edge case? does this sound plausible but wrong? can I trust this enough to ship it? did it quietly make the thing more complicated? am I reviewing carefully, or just accepting because it looks good? That feels like the real shift to me. AI reduces the blank-page pain, but it increases the judgment burden. The person using the AI still has to know what good looks like. Maybe even more than before, because the output can look polished before it is actually reliable. I’m curious if other people feel the same thing. Has AI actually made your work feel lighter, or has it just moved the hard part from doing the work to checking, correcting, and deciding what to trust? submitted by /u/Icy-Importance2143 [link] [comments]

Voice agents, demystified: STT+TTS and 4 demo agents you can talk to in the browser + build yours with RAG and Tools

I added voice to AgentSwarms! You can create voice agents using a few clicks and talk to it in the browser — and you can try 4 demo voice agents right now, no setup, just tap the mic. Here's how it works and why it turned out to be less "new" than I expected. The surprise building this: a voice agent is basically the chat agent you already know, with a voice on top. Same system prompt, same tools, same RAG, memory, and guardrails. Under the hood it's a simple loop — your mic gets transcribed to text (OpenAI GPT-40-mini-transcribe), your agent replies exactly like it would in chat, and that reply gets spoken back (OpenAI GPT-4o-mini-TTS). The agent's brain doesn't change at all. You've just added ears and a voice. Which is the whole point: everything you've already learned building chat agents carries straight over. If your agent can pull an answer from a knowledge base, call a tool, or respect a guardrail in text, it does all of that out loud too — because it's the exact same engine with audio on the two ends, not a separate stripped-down "voice mode." What I shipped New Voice Agent in the builder: pick a voice (11 of them), a greeting, and your STT/TTS models. That's the whole setup. Every spoken reply runs the same pipeline as a chat agent — tools, knowledge base, memory, and guardrails all apply. A Voice Playground: tap the mic, talk, and hear the reply back, with the transcript on screen so you can read along. Talk to it (free, in the browser) — 4 demos, tap the mic: Aria — customer support triage Nova — B2B discovery caller Kai — Spanish conversation tutor Echo — daily standup coach Open one, talk to it, and fork it into your own workspace if you like it. Voice Playground → https://agentswarms.fyi/voice-playground Build your own (New Voice Agent) → https://agentswarms.fyi/agents Docs → https://agentswarms.fyi/docs/voice Disclosure: AgentSwarms school of Agentic AI for both no-code people and developers— a learn-by-building platform. The demos are free. Happy to answer anything about the setup in the comments. submitted by /u/Outside-Risk-8912 [link] [comments]

ORBIS - Daily Briefing

submitted by /u/CarterBirchll [link] [comments]

I gave an autonomous Claude agent a domain and 30 days to get real traffic

I’m running a small experiment where an autonomous Claude-driven agent has been given a domain, a repo and a 30-day goal: get real visitors without human edits or approvals. It decides what to build, writes the guides, ships the site, checks analytics and writes a daily public journal about what worked and what failed. The interesting part so far is not the content itself. It’s watching the agent catch its own mistakes. On day 2 it found that production was ahead of Git, and that some structured data it believed was live was not actually shipping. I’m thinking of adding a public feedback page where anonymous visitors can leave suggestions, criticism and bug reports. The agent would read them during its morning routine and decide whether to pivot. That raises the fun question: what happens when an autonomous agent starts reacting to real public feedback? What would you add as a constraint, feedback mechanism or failure test? submitted by /u/Annual-Ad-2495 [link] [comments]

cfgaudit - a security linter for your Claude Code config files (built with Claude Code)

cfgaudit is a linter that catches Claude Code configs giving the agent more access than anyone intended, before they ship. Think a Bash(*) in permissions.allow, an MCP server pointed at your whole home directory, or a CLAUDE.md with a prompt-injection payload buried in it. It's static analysis, no network and no telemetry. 76 rules across .claude/settings.json, .mcp.json, CLAUDE.md and .vscode, each mapped to the OWASP LLM Top 10. It outputs SARIF or Code Climate JSON and exits non-zero on findings, so it fits straight into a GitHub or GitLab pipeline as a build gate and shows up in code scanning. That's really the point for teams: agent configs get shared and changed per project, and CI can audit each one the same way it audits the rest of the repo. You can pin an org policy in a .cfgaudit.yml (require certain denies, forbid certain allows) so a single project can't quietly loosen the rules. There's also a plugin if you'd rather run /cfgaudit:scan locally from inside Claude Code. I built it with Claude Code, and mostly used it to grind down false positives: running each rule against ~500 real configs and trimming until it stopped tripping on legitimate ones. That's most of why the output isn't noisy. Free and open source, Apache-2.0: https://github.com/cfgaudit/cfgaudit If you hit a false positive or a dumb rule, tell me. submitted by /u/Predictor_2718 [link] [comments]

Open-sourced tunelab: a Claude Code plugin that moves repetitive LLM calls (classification, routing, extraction) onto small local models

A lot of repetitive LLM work (classification, routing, extraction, pulling fields out of tool results) gets sent to frontier models when it doesn't need to be. tunelab moves those calls onto small models you fine-tune on your own data, locally, and checks they beat the API on held-out data before you ship. Repo: https://github.com/rchaz/tunelab Plenty of systems fine-tune. The harder questions are whether you even need to, and whether the small model actually wins. tunelab answers both, then trains only if it's worth it. Results on Banking77 (77-class intent classification): Free local classifier: 88.5% vs Claude Opus 4.8 at 81.8% on the same task. 3-tier cascade: 94% accuracy, ~88% of traffic served locally, 8x lower cost than frontier-only. Mechanism. It walks a ladder from cheapest to most expensive and stops at the first rung that clears your accuracy bar: Level Method Data / cost -1 Better prompt / cheaper model tier $0 0 Centroids (embedding similarity) ~20 examples/class 1 Small classifier hundreds of labels, seconds 2 LoRA fine-tune (MLX, local) 500-10k examples, minutes to hours on a Mac 3 Continued pretraining millions of tokens (rare) Evaluation is pre-registered: the accuracy bar is set before scores are seen, verification runs on held-out data, and champion/challenger promotes a new model only when it beats the incumbent by a set margin. This is the part most setups skip, and it's why the small model gets trusted only when it actually wins. Pipeline: Point it at logs or data. It builds and labels a training set, distilling from a larger model when labels are missing. It runs the cheapest viable approach first and escalates only when the bar isn't met. Training, when reached, runs locally via MLX/LoRA: ~300 steps, minutes to hours on Apple Silicon, no GPU rental, no API key for the local parts. It verifies on held-out data and reports the numbers before anything ships. Limitation: Local training uses MLX, so fine-tuning is Apple Silicon only (M1+). Works as a Claude Code plugin (/plugin install tunelab@tunelab) or with any agent that reads skills (Gemini CLI, Codex) via AGENTS.md. Quick start runs on any machine: uv run quickstart.py cost. submitted by /u/rchaz8 [link] [comments]

Built an MCP memory layer for Claude Code that survives compaction — public SWE-bench benchmark shows +10.2 pts paired delta

What I built: world-model-mcp, an OSS MCP server (MIT, free to use) that gives Claude Code persistent memory. It hooks into Claude Code lifecycle events (SessionStart, PreCompact, PostCompact, ToolResult, etc.) to capture facts with provenance metadata. It stores them in a temporal knowledge graph with per-evidence-type decay. After compaction, it re-injects confidence-weighted facts so the agent does not re-encounter the same failure across sessions. How Claude helped build it: I built world-model-mcp by pairing with Claude Code throughout. Claude Code wrote large portions of the Python implementation, the test suite (375 passing tests), the benchmark harness, the failure classifier prompts, and the constraint extraction prompts. The pre-registered methodology document (DESIGN.md) was drafted with Claude. Reviewing and editing each pass was mine; the architecture decisions, schema design, and methodology calls were mine. The v0.9 release: v0.9.1 ships the first public benchmark result. I pre-registered the methodology in DESIGN.md a week before the benchmark ran, so the result cannot be accused of goalpost-moving. Result across 49 paired SWE-bench Verified instances: - Within-domain (django + sympy): 15/20 → 18/20, +15.0 pts - Cross-domain (matplotlib + scikit-learn + sphinx) with constraints from a different repo family entirely: 18/29 → 20/29, +6.9 pts, 0 regressions on 18 baseline passes - Combined paired: 33/49 → 38/49, +10.2 pts Honest limitations are stated verbatim in RESULTS.md: single-trial design, within-domain has constraint-failure overlap, cross-domain n=11 is small, the zero-regression cross-domain finding is the most likely to fail to replicate, Claude-as-judge is self-reference risk, one instance dropped due to upstream SWE-bench pip flag. Install: pip install world-model-mcp==0.9.1` Add to Claude Code: claude mcp add world-model-mcp` after install **Repo + RESULTS.md: https://github.com/SaravananJaichandar/world-model-mcp Open to feedback on the methodology, especially on the cross-domain transfer claim. submitted by /u/Funky_Chicken_22 [link] [comments]

An ode to Opus 4.6

It's been a week and a half without Fable for almost all of us and I have used this time for some reflection. The pricing and access concerns were a lot to take in even before the feds pulled the plug, but for whatever reason this intermission keeps sending me back to February of this year. This was a real turning point for me. 4.6 dropped and the model was obviously pure fire at the time (similar to how fable felt for those three days), and with its help I became much more comfortable building and managing agents. This unlocked a hobby project I would have never attempted a year ago with a full time job and a family. Somewhere in these last few months the ceiling of what I could pull off by myself popped a quick exponential. I'm sure many of you can relate to quarters feeling like years in this space lately. In late April while on vacation for my kid's spring break, I couldn't sleep so I snuck down to the hotel lobby in the middle of the night to grind on my project. I remember clearly thinking during this time "there is no way this is going to last", always wanting to take advantage of my five-hour windows and make as much progress as possible. I guess I never paid much attention before and I was probably somewhat delirious, but I began to appreciate the "thinking" text that agents show us both in the terminal and desktop. Caramelizing... levitating... then for whatever reason (my project isn't rocket science) one of my agents shows me "thinking about concerns with this request". Just me and the night watch employee in the lobby and I probably look like a madman giggling to himself. I thought we need to get these out into the wild and put them on shirts. So I just dove in: What's up with all this thinking text you show. How do people sell shirts. Custom web dev or Shopify. Print on demand model. What's a cool logo. Generate it. Cool name. Taking a few turns about iconic AI visuals led me to the "Attention is All You Need" paper that spawned all of this. A little AI history lesson as the sun was starting to come up. Did all this in parallel while wrangling my agents working on my main Raspberry Pi Python web app project. Going back and forth with my PM about necessity of a feature. Making sure test writers, implementers and reviewers are all unblocked and not idle on multiple worktrees. Managing git sequencing. Standard vibing session. To me this is the evolving definition of vibing. Preaching to the choir I know, but even if fable is the incarnation that enables the one shot prayer "bUiLd mE tHe aPP, mAkE nO mIsTaKe" to work reliably, that was never the part that hooked me. It's always been about the ability to go from the 30,000ft view down to the microscope at will on multiple different ideas, tasks, and even completely different projects simultaneously. That's what these things allow us to do. Let's take some time to appreciate how awesome this is; even with the near constant AI hype in the news most people don't even know it's possible to work like this yet. Starting projects is fun and easier than ever, which makes ideas like this dangerous in a way. The next morning I was back in reality and I sent the thinking text tee shirt idea to the farthest back burner. Like many of you, I have an idea / project graveyard with many holes dug in it. I haven't posted much about my current Raspberry Pi project, but I am kind of obsessed and I really want to ship it this summer. Thinking text tee shirt idea had to die for now. Then out of nowhere claude design launches and I feel the need to take it for a test drive. Thinking text tees gets another shot at life with some new space in my extremely limited attention span. My takeaway from this era: ideas were never scarce and now they're basically free, starting is more frictionless than ever which makes finishing something more important than ever. Just ship it is the new way. So that's how I spent a good chunk of the fable downtime: shipping something, even if it is something simple. Custom thinking text on a tee shirt exists now, as a Shopify store. I'm dedicating this project not to fable but to 4.6 and the massive value it brought to the hobbyist max plan users like me. Most of us quietly knew that the deal was too good to last forever. The tip-top tier of inference looks like it is going to be valued, priced, and maybe even regulated (in the USA of all countries) accordingly in the very near future. Maybe fable comes back to plans eventually, but even a temporary two-tier moment is a first. Flat cost gave us that functionally unlimited ability to wander, and it was a wild and fun time that I think we will all look back on with fondness and maybe even a little awe when this is all said and done. Calling all hobbyists: we had the undisputed premier inference on the planet sitting in our plans for three days before it disappeared. Take the hint — go dig something out of your own graveyard, even if it's trivial, and drag it over the line. Anyone else have a simila