Michael Kisilenko recently shared something that caught my attention: "I just shipped the biggest update since the beta launch 😱 What's new: → Completely redesigned UI → Next-generation coding agent (significantly faster) → Smoother onboarding flow (connect with Google or GitHub)".

That punchy checklist format is classic product-update energy, but it also signals something deeper: Anyx is treating the beta like a conversation, not a waiting room. Michael also added that GitHub integration is live, an official Hackathons program has launched, and that he wants direct feedback via DMs. In other words, the product is moving fast, and the feedback loop is intentionally short.

In this post, I want to expand on what Michael shared and why each line item matters if you are building with AI coding tools, evaluating agentic workflows, or simply trying to ship software faster without sacrificing control.

The real story behind "the biggest update"

Big updates can be cosmetic, foundational, or both. Michael’s list implies both: a redesigned UI and onboarding are about usability, while a faster "next-generation coding agent" and GitHub co-working are about capability.

The common thread is friction removal. If an AI coding product is going to be used daily, friction shows up in predictable places:

Understanding what the agent is doing
Getting started quickly
Trusting changes enough to merge them
Collaborating with the agent inside the tools engineers already live in

Michael’s update targets each of those bottlenecks.

Key insight: Speed is not just latency. It is fewer steps between intent and a merged pull request.

A completely redesigned UI: why it matters for agentic tools

Michael led with: "Completely redesigned UI". That might sound like a surface-level change, but UI is where trust is built or lost with an AI agent.

When an agent writes code, it is effectively making a long chain of micro-decisions. A good UI helps you answer:

What is the agent planning to do next?
What files did it touch, and why?
What assumptions is it making?
How do I steer it without wrestling it?

A redesign is often a signal that the team learned from real beta usage. Early UIs frequently over-index on demos. Beta-informed UIs tend to optimize for repeatable workflows: clear diffs, transparent reasoning, easy rollback, and quick iteration.

If Anyx’s UI redesign makes it easier to review and control agent output, that is a meaningful shift from "wow" to "work".

A next-generation coding agent (significantly faster)

Michael also called out a "Next-generation coding agent (significantly faster)". People often interpret this as raw model speed, but in practice "faster" can come from multiple layers:

Better task decomposition (fewer dead ends)
Smarter retrieval of project context (less guessing)
Improved tool use (tests, linters, build commands)
Caching and incremental edits (less repeated work)

For teams, speed changes behavior. When a coding agent is slow, you batch requests, tolerate lower precision, and context-switch while you wait. When it is fast, you iterate like pair programming: prompt, review, adjust, rerun.

That is where productivity actually compounds.

What to look for when an agent claims "faster"

If you are trying Anyx in beta, I would evaluate speed in terms of outcome time:

Time from request to a working branch
Time to pass tests
Number of back-and-forth prompts needed
Quality of incremental edits vs full rewrites

Fast wrong code is still slow. Fast correct code changes everything.

Michael mentioned a "Smoother onboarding flow (connect with Google or GitHub)". This is the unglamorous part of product building that often determines activation.

AI coding tools have a steep "first project" cliff:

Users need a repo or a template
They need permissions configured correctly
They need to understand the agent’s boundaries

Social sign-in will not solve everything, but it reduces immediate friction, and it signals an intent to meet users where they are. If onboarding is genuinely smoother, it should do two things:

Get you to a meaningful result quickly
Teach you the product’s mental model as you go

Michael’s line "Watch the video to kick-start your first project in 90 seconds" reinforces that goal: short path to first value.

GitHub integration: co-work alongside the agent

This, to me, is the most strategically important bullet: "GitHub integration is finally live (co-work alongside the agent)".

GitHub is where software collaboration is already standardized: branches, pull requests, reviews, CI checks, and discussions. When an AI agent lives outside that loop, you get a translation problem:

You copy patches around
You lose context between chats and diffs
You struggle to audit what happened

GitHub-native workflows reduce that gap. "Co-work alongside the agent" suggests a model where the agent is not a magic box, but a teammate operating in the same repo mechanics you already trust.

Why GitHub integration increases trust

Trust comes from auditability:

Clear diffs in PRs
Commit history you can inspect
CI results tied to changes
Review comments that capture decisions

If Anyx can help generate PRs that are small, testable, and well-explained, it will fit into real engineering culture, not just prototypes.

Key insight: The best agentic tooling does not replace the team’s workflow. It inhabits it.

The official Hackathons program: community as a product lever

Michael also said: "Launched the official Hackathons program. (If you organize one, DM asap!)".

That is more than marketing. Hackathons are high-signal environments for developer tools because they stress-test:

Setup time under pressure
Documentation clarity
Reliability across diverse projects
Support responsiveness

They also create a rapid feedback pipeline. In a hackathon, participants do not politely work around rough edges. They hit walls fast, ask direct questions, and reveal the sharp corners.

For Anyx, formalizing hackathons can accelerate learning, surface edge cases, and build advocates who have actually shipped something with the tool.

Still in beta: why the feedback request is the point

Michael closed with: "We're still in beta, and I would love to get your feedback. Please DM with whatever feature requests or ideas you have - Help Anyx improve and shape the future."

That is the posture that tends to create durable products: ship, listen, iterate, repeat. For AI coding tools especially, the "future" is shaped by countless small UX decisions:

How the agent asks clarifying questions
When it runs tests by default
How it handles refactors vs quick fixes
n- How it explains uncertainty

If you are giving feedback, make it actionable

If you decide to DM feedback (as Michael invited), the most useful input is specific:

Your repo type and stack (for example, Next.js, Python, monorepo)
The task you attempted (bug fix, feature, refactor)
What you expected vs what happened
Screenshots, logs, or PR links if possible
The "moment of friction" that made you stop

This helps the team separate model limitations from product or workflow issues.

A quick note on why this LinkedIn post worked

Even though it is an AI product update, it is also a strong piece of LinkedIn content. Michael’s post hits several patterns that often drive viral posts in B2B developer audiences:

A clear hook ("biggest update since the beta launch")
Skimmable bullets that communicate value fast
Concrete features, not vague vision
A simple call to action (watch a 90-second video, DM feedback)
Community invitation (hackathons)

If you are building in public, this is a good reminder that content strategy does not need to be complicated. Tell people what shipped, why it matters, and what you want from them.

Where Anyx’s update points next

Putting Michael’s bullets together, the direction is clear: Anyx is moving toward a tighter loop from idea to PR, with less setup friction and more collaboration inside GitHub.

If the redesigned UI improves transparency, the faster agent improves iteration, and GitHub integration improves trust, the product becomes more than a novelty. It becomes infrastructure for everyday development.

And because it is still beta, early users have real leverage. When founders actively ask for feature requests, the most helpful move is to test the product on real work and report back with specifics.

This blog post expands on a viral LinkedIn post by Michael Kisilenko, Anyx 👀. View the original LinkedIn post →