GitHub
GitLab
BitBucket
VS Code
Git
Treehouse
Pantheon
CodePen
Atono
Jira
Linear
Plane.so
Asana
Bugasura
Trello
Sanplex
Teams don't have a coding problem anymore. They have a context problem. AI made producing software cheap, which exposed product context as the scarce resource โ and agents can only reliably act on the context they can access.
Atono is a product engineering platform that keeps product context connected to the work. It spans the full loop โ plan, build, deploy, measure โ in one system: stories and epics, Scrum and Kanban workflows, feature flags, and real feature-engagement data.
The story is the hub. A single story carries its user story and acceptance criteria, the feature flag controlling its rollout, the usage data proving whether it worked, and the AI context โ design decisions, investigations, summaries โ that agents read. In a conventional stack those four live in four separate products, and context is destroyed at every handoff.
A locally-run MCP server exposes 41 tools to Claude Code, Claude Desktop, Cursor, VS Code/Copilot, Windsurf, and Codex, so agents read requirements, update workflow steps, document fixes, and write design decisions back without leaving the editor. Agent actions stay attributed and auditable, and AI-generated values are marked, so you always know what came from where.
A workspace glossary keeps your product's terminology in one place, so agents stop guessing at your domain language. Delivery metrics โ cycle time, burndown projection, velocity, estimated completion dates โ are computed from your team's actual throughput, and a staleness indicator flags stalled items automatically.
Atono integrates with Slack and GitHub, ships a Chrome extension for bug reporting and flag toggles, and imports existing work from Jira and Linear.
Best fit: post-MVP SaaS companies with 25โ250 engineers adopting AI-assisted development. Adopt it beside Jira or Linear, or consolidate work tracking, feature flags, and product analytics into one workspace.
Free for up to 25 users. Starter $19/user/month. Growth $39/user/month.
GitHub AtonoAtono's answer:
Atono is built using:
React for a fast, fluid frontend
Node.js + GraphQL for a flexible backend
PostgreSQL for structured data
Redis for caching and speed
Feature flagging engine built in-house
TailwindCSS for clean UI styling
Docker + Kubernetes for scalable deployment
Atono's answer:
Fast-growing devtool startups
Product-led SaaS companies
Engineering-led teams scaling past 10+ devs
Atono's answer:
Atono keeps the meaning behind the work attached to the work itself.
On most teams a feature's requirements live in one tool, the flag controlling its rollout in a second, the usage data proving it worked in a third, and the reasoning behind all of it in someone's head. Atono puts all four on the same object โ the story. That isn't a bundling convenience; it's what lets product context survive a handoff instead of being rebuilt from scratch at every one.
That matters more now than it did two years ago, because AI agents can only act on context they can reach. A 41-tool MCP server hands your product context directly to Claude Code, Cursor, and Copilot, so they work from what your team actually decided rather than inferring it from the code.
Atono's answer:
What you're usually replacing isn't one product โ it's a work tracker, a feature-flag service, and a product analytics tool, plus the manual effort of keeping them in sync. Choose Atono if:
Your AI tools keep producing almost-right work. Output that looks right, passes review, ships, and fails weeks later. That's a context problem, and it's the one Atono is built for.
You want flags that belong to the feature. The flag lives on the story that defines it โ no third-party service, no ID-matching between systems.
You want to know whether what you shipped worked. Usage data sits on the story that produced it, not in a separate tool someone has to go check.
You need to know why to believe a piece of context. Atono records where a decision came from and what changed it. Retrieval and embeddings can surface a connection; they can't tell you who decided it or on what evidence.
Honestly, when not to choose us: if you want the fastest, most polished issue tracker, Linear is excellent and we don't out-build it on speed. If your team is small enough that everyone still holds the context in their heads, you may not feel the problem yet.
Atono's answer:
Post-MVP SaaS companies with roughly 25โ250 engineers who are adopting AI-assisted development. Specifically:
VP Engineering โ usually the buyer. Feels it as rework, inconsistent output across teams, and onboarding cost. Engineering managers and product managers โ need delivery visibility and a place where intent survives a handoff. Developers working with AI agents โ tired of re-explaining the product to a tool that forgot it last session.
The most common starting point is a team leaving Jira. The alternative we actually displace first is the do-it-yourself version: CLAUDE.md files, a structured repo, a Notion doc the agents can't reliably read. It works at five engineers and breaks at 250.
Atono's answer:
"Atono" means unstressed. That was the original goal โ after years of tools where the tool became the work, we wanted planning and shipping to feel calm.
Building it, we hit a bigger problem than clutter. As AI coding tools arrived, we watched teams get faster at producing code and no better at producing the right code. Agents were confidently building things nobody had asked for, because the reasoning behind the work had never been written down anywhere they could read.
That reframed the product. The thing worth protecting isn't screen space โ it's product context: what you're building, why, and what constraints it has to honor. Atono captures it as work happens instead of in a document that rots, and keeps it connected to the stories, flags, and outcomes it belongs to.
Based on our record, GitHub seems to be more popular. It has been mentiond 2475 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.
# video-meta.yml Topic: > Walking through how we cut cold-start time on a Lambda-backed GraphQL API from 2.4s to under 400ms, including the two things that didn't work. Target_keyword: lambda cold start optimization Audience: backend devs who already ship serverless, not beginners Model_channels: - the three channels currently ranking for this keyword Links: repo: https://github.com/... slides:... - Source: dev.to / about 17 hours ago
Import struct, json, urllib.request REL = "https://github.com/{owner}/{repo}/releases/download/{tag}/" PART = ["...part1.zip.001", "...part2.zip.002", "...part3.zip.003"] SIZE = [1992294400, 1992294400, 1893639808] # from the releases API Def grab(part, start, end, out): # HTTP range fetch req = urllib.request.Request(REL + PART[part], Headers={"Range":... - Source: dev.to / 5 days ago
Is published at https://github.com/.keys so an SSH server to which you connect could do a reverse lookup. This is the reason why my ~/.ssh/config has those 2 lines at the end:- Source: Hacker News / 13 days agoHost *.
All of this assumes you can actually inspect what the agent did โ the real inputs after resolution, the real tool outputs, the real intermediate steps. That is the other half of the workflow. AgentLens captures the trace: every model and tool step, resolved inputs, raw outputs. agent-eval scores and gates the output; AgentLens gives you the unforgeable, agent-didn't-author trace data for Tier 1+2 to score against... - Source: dev.to / 13 days ago
# git: the API token, plus the credential used for the push Kubectl create secret generic foreman-github \ --from-literal=GITHUB_TOKEN="$GITHUB_TOKEN" -n foreman-system Kubectl create secret generic foreman-git-credentials \ --from-literal=token="$GITHUB_TOKEN" -n foreman-system Helm upgrade foreman llmkube/foreman -n foreman-system --reuse-values \ --set agent.githubToken.secretName=foreman-github \ ... - Source: dev.to / 14 days ago
GitLab - Create, review and deploy code together with GitLab open source git repo management software | GitLab
Jira - The #1 software development tool used by agile teams. Jira Software is built for every member of your software team to plan, track, and release great software.
BitBucket - Bitbucket is a free code hosting site for Mercurial and Git. Manage your development with a hosted wiki, issue tracker and source code.
Linear - Streamlined issue tracking for software teams
VS Code - Build and debug modern web and cloud applications, by Microsoft
Plane.so - Open-source project management tool to manage issues, sprints, and product roadmaps with peace of mind.
