Haystack Analytics
LinearB
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CodeClimate
Athenian
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Topolog
monday.com
Asana
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Trello
MS Project
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Haystack is a real-time delivery analytics platform designed for engineering leaders like CTOs, VPs of Engineering, Directors of Software Engineering, and Engineering Managers. Haystack provides actionable insights that enable data-driven decision-making, aligning engineering performance with business objectives. Haystack platform integrates seamlessly with essential developer tools like GitHub and JIRA, offering a comprehensive view of team productivity and delivery efficiency.
Leading companies like AngelList, Shutterstock, Schneider Electric, and many more trust Haystack to optimize their development processes. By transforming historical Git data into objective insights, we help you identify bottlenecks and visualize trends, ensuring timely project delivery and sustained business growth. Our analytics dashboard allows you to monitor critical metrics such as cycle time, making it easier to spot inefficiencies before they escalate into costly delays.
Haystack helps engineering leaders to mitigate risks and improve workflow efficiency. With a unified view of the entire delivery lifecycle, you can track KPIs, compare performance trends, and make informed decisions that drive measurable outcomes. Our platform goes beyond merely measuring productivity; it equips you with the tools to foster continuous improvement and innovation within your teams.
Designed to scale with your organization, Haystack is the competitive advantage that data-driven engineering teams need to thrive. By leveraging analytics, you can transform your engineering operations, enhance collaboration, and accelerate your path to market success. Join top companies in harnessing the power of Haystack for a more efficient and effective engineering process.
Topolog turns any goal into a dependency graph and schedules your days around it. You get a structured plan, a completion spectrum, and a task list that adapts as you mark them done. Every plan is a real program, so the dates and odds are computed, not guessed.
Haystack Analytics TopologHaystack Analytics's answer
Engineering Leaders and Managers
Topolog's answer:
Anyone running a goal with real dependencies and real stakes: technical project managers, engineering managers, founders, and ambitious individuals planning complex personal projects like home renovations, album productions, or marathon training. The unifying characteristic is feeling the pain of planning tools that lie about deadlines. Topolog is for people who want to know their actual odds, not a false sense of certainty.
Topolog's answer:
Built by a solo founder with 14 years across Meta, Media.net, Amazon and others. After watching countless projects miss deadlines, not from incompetence but from tools that gave one fake date, I set out to build a planning engine that takes uncertainty seriously. The result is Topolog: a formally total scheduling language, a deterministic Monte Carlo engine, and a Bayesian self-tuning scheduler. Built entirely solo with Claude Code and Devin as AI engineering partners. Zero VC, zero team, 100% ownership.
Topolog's answer:
Every other planning tool gives you one deadline, the one you'll miss. Topolog gives you the full picture: a dependency graph that knows what blocks what, a Monte Carlo completion spectrum showing your real odds, a critical path that updates as you execute, and a budget tracker tied directly to your probability of success. MS Project has critical path but no probabilistic engine. Monday and Asana have boards but no complete dependency model. AI tools hallucinate dates. Topolog computes them.
Topolog's answer:
Topolog treats every plan as a program. Plans are written in TOL (Total Orchestration Language), a formally total, decidable language where the scheduler and Monte Carlo engine compute dates and probabilities deterministically. The AI drafts structure but never touches the maths. You get a completion spectrum (a probability distribution over outcomes), honest deadline ranges (a floor and a ceiling, never one date you'll miss), and a Bayesian self-tuning scheduler that learns your real pace from timestamps alone. The planning language is public, you can author plans with any AI and run them through Topolog's engine.
Topolog's answer:
Topolog is a TypeScript-first web app built around a custom stochastic-planning engine:
Frontend: Next.js 15 (App Router) with React 18 and TypeScript, styled with Tailwind CSS. The interactive plan canvas uses dagre / ELK (elkjs) for graph layout.
Core engine: an in-house DSL ("TOL") plus a Monte Carlo stochastic-forecasting engine, written in pure isomorphic TypeScript so it runs identically on the server and in the browser.
Backend & data: Supabase (PostgreSQL, auth, and SSR), with the API layer on Next.js route handlers. Stripe handles billing.
AI authoring: a model-router layer that calls GPT (OpenAI), and Mistral for plan authoring and review.
Infra & quality: deployed on Vercel (Analytics + Speed Insights), error monitoring via Sentry, and tested with Jest + Playwright.
Based on our record, Haystack Analytics seems to be more popular. It has been mentiond 2 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Heads up: site is not loading. Ios Safari & macOS Chrome. Mixed Content: The page at 'https://usehaystack.io/' was loaded over HTTPS, but requested an insecure favicon 'http://www.usehaystack.io/favicon.ico'. This request has been blocked; the content must be served over HTTPS. - Source: Hacker News / over 5 years ago
Hey HN! I'm Julian, co-founder of Haystack (https://usehaystack.io). Weโre building one-click dashboards and alerts using Github data. While managing teams from startups to more established companies like Cloudflare, my cofounder Kan and I were constantly trying to improve our team and process. But it was pretty tough to tell if our efforts were paying off. Even tougher to tell where we could improve. We tried... - Source: Hacker News / over 5 years ago
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