1. SprintFlint extends to your harness - developers can connect with SprintFlint MCP.
2. Humans can use Runners to Autoplay tasks using your Fully Configurable Harness.
3. Managers can manage tasks and sprints directly from SprintFlint's AI Chat.
4. Kanban is 2 clicks away.
We've been building https://t.co/xApoUpj3zo with @AnthropicAI's AI; Working to bring the best harness for project management & execution for startups & organisations in a professional agentic setup.
Fable is a gamechanger.
Introducing Claude Fable 5: a Mythos-class model that we’ve made safe for general use.
Its capabilities exceed those of any model we’ve ever made generally available.
@mattpocockuk this is exactly the thing we're building. the review queue turned out to be the real bottleneck for us, not the task queue. agents clear tasks fast, deciding what's actually worth shipping is the slow part.
same at the team level. AI ships fast once a human has actually decided what's worth building. adding more agents doesn't fix a vague plan, it just produces wrong code faster.
One thing to notice: vibe coding helps you build faster, not learn faster.
If you are in the learning phase, don’t rely too much on AI. Build the foundation first, then use AI to ship faster.
Everyone will run a team of AI Agents on their PC by end of 2026.
NVIDIA RTX Spark with 128GB unified memory is built for always-on agents that run 24/7 locally.
HUGE for Hermes and OpenClaw Agent. Step-by-step guide to run local coding agents.
Coding is basically the pinnacle of what you could reasonably automate with AI, and yet we still need human engineers to oversee agents for them to be effective.
The AI models are trained on an incredible amount of sophisticated code. The users are highly technical and can use the latest tools quickly. The work is “verifiable” because you can test an app. The outcomes are often removed from the quality of the code (you can have sloppy code but the app can still work). And the context for the agent is often already digitized and sitting in the codebase.
That’s an incredible amount of benefits that AI coding agents get to work with. Some of those apply to knowledge work, but most don’t in areas where the work needs to be fully reviewed to be useful, or where data isn’t as abundantly digitized. This makes the job for agents in knowledge work more complicated.
So if with all of that, engineers still remain in very high demand, the risks are going to be less than what’s perceived for other areas of knowledge work. Agents will let people do far more than they did before, but the people don’t go away.
running sprints with AI doing the implementation, the bottleneck stopped being how fast code gets written. it's how clearly the ticket was scoped. a vague ticket now just produces a confidently wrong PR in minutes.
I just submitted SprintFlint on NitPickr. We will give feedback on your app if you give feedback on ours.
Request an exchange here: https://t.co/SHPElmJc9I