... problems Fourier series, the Lorenz attractor, and the Kakeya needle problem that reached the top of mathematics' honors this year. In two fresh sessions,  Grok 4.6 and Grok 4.5  received the identical questions, each answer a single runnable web page. The grader was Fable-5, a third model living in the same model picker, which ran all six answer files itself. Humans did exactly one job in this pipeline: invigilation. Final score:  90 to 70. The winner ... ... also used 15% fewer output tokens (11,702 vs

... parked on an external professional reviewer, an authorized member of your own team can open the pending review in the dashboard and submit the decision, so the job continues instead of waiting on the external round trip. A runnable example repo for every framework. Each is a real project with .lingo/config.json and translations committed clone it, run lingo link, and push: iOS , Android , Flutter , React + Vite , Next.js , Rails , Laravel , Markdown , Markdoc , OpenAPI , and TypeScript modules . Full changelog: lingo.dev/changelog ...

... exists to reproduce it? Comment from Raj Nagulapalle(@rnagulapalle): @akbar_b  Both are exactly how I think about it. Right now a confirmed repro is scoped to that fix-and-verify run the receipt is the durable, re-runnable artifact, but we don't yet auto-promote it into a standing regression suite. That promotion (a proven repro becomes a permanent guard) is the obvious next step, and it's the whole "remember what breaks" thread. Yes the reproduce ... ... edge than timing, honestly. ..today the generator works from the code path + known failure classes, not your real data distributions .. it doesn't peek at samples. What it can do is reason about what the code assumes vs

... harder problem for me isn't memory, it's trust in the agent's own status reports. When an agent says "fixed it, tests pass" or "yes that endpoint handles that case," how much do you actually check versus just believing it and moving on? I've gotten burned a couple times by an agent confidently describing behavior it never actually verified, it just pattern-matched to what a fix usually looks like. Curious what people's actual habits ... ... reduce. An agent saying fixed is not enough; the repo should expose an execution record that a human, CI job, or another agent can inspect without repeating the whole investigation. In an Ota-backed repo, the contract declares setup, runnable

... issue. I'm new to coding, so I'm learning as I go. I'll try to deny git at the permission layer. That should sort things out. Comment from Gal Dayan(@galdayan): @aadilghani  same read-only-vs-reversible line for me. the thing that changed it wasn't a rule I wrote, it was switching to a tool that asks per-action instead of per-session - so "let it rip" doesn't mean blanket auto-approve, it means ... ... tasks the agent completed autonomously without me needing to undo the work. after ten successes in a category, that category becomes auto-approve. one failure resets it to watched. slow to build but you know exactly what to trust vs

... GitHub not suggestions, actual commits One-click deploy to Railway & Vercel from the dashboard Agents send real emails outreach, campaigns, sequences Image generation built in for landing pages and content Generates full-stack apps (React + FastAPI) real, runnable code Market research, financial projections, and sales strategy all automated You give Agentica a company name and a one-line mission. The CEO agent breaks it down, delegates to 5 specialized agents, and they start executing autonomously. You watch ...

... canonical place the agent always reads but for the handful of operations that could cause real damage, enforce it with something that can't be ignored, not just described. Curious to see where Ota lands on that doc-vs-enforcement spectrum. Comment from Adamma(@faithada): @achille82  What you built from a totally different background is seriously impressive! I'll be sure to support on the 17th. On Ota: it is a repo execution contract that lives in your repo ... ... repo needs, what tasks exist, what is safe to run, and what should be verified after changes. In practice: `ota doctor` shows what is missing or risky `ota up` prepares the repo correctly `ota tasks` shows the approved runnable

... guess from scattered docs, scripts, and error logs. This is where Ota's `ota doctor` command is useful. `ota doctor` checks a repo against its readiness contract (the `ota.yaml` file that defines what the repo needs to be considered runnable and operationally ready), then surfaces what is missing, what is blocking readiness, and what the next safe action should be. For maintainers, that means fewer people rediscovering the same setup blocker from scratch. For contributors, it means the repo becomes ...

... built for people who already know them. The maintainer remembers the setup step that matters. The team knows which script to run first. Someone understands why the README says one thing but CI does another. The repo is runnable, but only because enough context lives outside the repo. That model breaks down when more software work is handled by CI jobs, remote runners, ephemeral dev environments, automation scripts, and coding agents. These systems do not know the history ...

... Ledger primitives (foundation): note commitments, nullifier derivation/set, simple commitment tree root Why this matters: If you re building toward post-quantum and shielded-chain designs, you need an execution layer that s deterministic and testable. VeilChain provides a real, runnable base in Zig so the next milestones (p2p connectivity, persistence, shielded transaction circuit/proofs, full RPC surface) can be added incrementally without losing correctness. How to try it (from the repo): ```bash zig build zig build test zig build test-integration ...

... Turns out, professionals can turn their real workflows into runnable AI systems. We call them Playbooks. And since our launch, people have already shipped some seriously cool stuff: Growth strategy execution PM sprint planning Scrum facilitation Trading signal workflows Research synthesis pipelines These aren't just docs or checklists. They're structured, multi-step systems that actually run. You bring the methodology, Leapility makes it executable. The Leapility Playbook Hackathon is live right now We're awarding 16 winners with ... ... live Q&A session. Your logic is the product. Come build with us. What process and scenarios would YOU turn into a playbook? Drop it below Comment from 007(@yifei_chen5): The idea of making professional workflows actually runnable

... faster, cloud scheduling algorithms cutting costs 40%, an evolved ARC-AGI agent going from 32.5% 89.5%, CUDA kernels beating baselines, circle packing outperforming AlphaEvolve's solution, and blackbox solvers matching andOptuna. pip install gepa |  Detailed Blog with runnable code for all 8 case studies  |  Website ...

... CodeRio converts Figma designs into runnable React projects and validates visual accuracy through automated screenshots and comparisons. Design-to-code tools are everywhere, but visual accuracy and engineering usability are still hard problems. CodeRio is an open-source Figma-to-React automation tool that focuses on what happens after code generation . Instead of stopping at static output, CodeRio runs the generated project, captures real screenshots, and compares them with the original Figma design. Visual differences are quantified, visual reports ... ... care about UI accuracy Teams bridging design systems and real production code Designers who want implementation results they can review visually Tech highlights Multi-agent pipeline (design parsing, code generation, validation, optimization) Visual comparison with pixel-level feedback Fully runnable

... code visual interface that anyone can use. Even so, users can download the model weights and fully own the models they generate. That said, our end goal is not the cloud. We re actively working toward making everything runnable locally or deployable on the user s own infrastructure, without us touching or intermediating their data or models at all. This includes research into model compression and optimization, so these systems can run on as many personal machines as possible ...

... still alien to them. After digging deep, I realized low adoption probably isn t because agents suck. I ve tried a lot of open-source agents that are actually useful. The real issue is understanding, delivery, and the reliability vs. complexity paradox. First, the vast majority of agentic apps require you to deploy or set things up, of course including preparing your API keys. With requirements like that, you can basically say bye-bye to normal users. Sure ... ... this help people discover and benefit from genuinely useful agents? And will it push builders to create more interesting apps, instead of only giant super agents getting adopted? So here s the invitation. If you already have a runnable

... code security scanners: Switching between IDE and clunky dashboards to check vulnerabilities Wasting hours sifting through false positives Getting vulnerability alerts with zero guidance on how to actually fix them I stumbled on an IDE extension (ZAST Express for VS Code/Cursor) last week that s amazing for my workflow. The Proof of Concept (PoC) feature is what sold me instead of just flagging issues, it gives runnable