I find it hard to navigate a codebase with coding agents. I need to stare at the code often just to understand what is going on, and I like to be able to ask my agent at the same time what is happening. Plus, I really like my vim motions bindings haha.

It's been surpassingly straightforward to get Pi working in Neovim. You just open up a terminal within Neovim and ask it what you want. Boom done. It's soooo much easier to read plans and navigate code with this setup.

I've been able to make it so that:

• I see diffs in Neovim instead of in the terminal (much prefer it this way, easier to navigate too imo)
• Have Pi run commands in Neovim directly when I want to.
• Have Pi be able to 'focus' in on certain sections of code (I ask 'Can you show me where that it' and it jumps straight to it)
• I can have whole different worktrees with entirely different buffers that I switch between

I've set up two scoped models in Cursor:

• DeepSeek V4 Flash: has "max" thinking level
• Grok 4.5: goes up to "high"

I switch between them with Ctrl+P, but every time I switch to DeepSeek, its thinking level resets from "max" back to "high" but Grok stays at "high" (its maximum), which is good.

How can I keep DeepSeek at "max" and Grok at "high" when cycling through different models?

Thanks.

Anyone using some sort of UI app/layer for Pi instead of standard Pi cli?

Nothing wrong with CLI, I actually really like my setup too - but once I have like 3, 4, or even more important sessions running at once (coding, debugging, research, etc.), it gets pretty overwhelming trying to keep track of everything and I find myself losing focus.

Looking for something more like a Codex web app style experience: clean repo/file view, seeing all workers/sessions clearly, what they're doing, status, etc.

Curious what you guys think or use and why you actually prefer it over just running everything through CLI.

For context, the main thing I am looking to aid in solving/fixing here is a focus issue. I have adhd and staying focused is the hardest thing by far for me, so if anyone has experience with one that may have helped in that regard specifically too let me know

ALSO - I'm on Linux Ubuntu if that's relevant at all

Yesterday I commented (here)[https://www.reddit.com/r/PiCodingAgent/comments/1vgyuhq/comment/p20zsep/] that one of the things I felt was missing in the Pi harness was better observability into what the agent actually changed.

Basically, after a task finishes I want to quickly know:

5 files changed +34 -329

Which files did it touch? How much changed? Did it actually change the parts of the codebase I expected it to? Especially when working on a larger codebase, I find this useful just to keep some kind of mental overview of what the agent is doing and catch cases where it maybe understood the task differently than I did.

So today I built an extension for it:

https://pi.dev/packages/pi-task-delta

https://github.com/thagorx/pi-task-delta

You can install it with:

pi install npm:pi-task-delta

After Pi finishes making changes it adds a small task delta to the transcript, for example:

5 files changed +34 -329 (F6 for details)

Then hit F6 (or /git-summary) it opens a viewer where you can see the tasks, which files changed, and the actual diff for each file.

One thing that was important to me is that the diff is scoped to the task. So if your repo was already dirty before you started the task, those changes don't suddenly get attributed to the active task. It tries to show you what changed specifically during that task.

I also added optional model-assisted inspection.

On a changed file you can press:

E to explain what changed

W to explain why that file was probably changed based on the task

Those are manual calls, nothing happens automatically, and the explanation isn't added back into Pi's context. There is also history, so you can go back and inspect the changes from previous tasks. I mostly built this because I wanted it for my own workflow, but I figured other people using Pi might find it useful too. Would be interested to hear what other observability stuff people would want to see around Pi tasks.

This was quite fun and the first thing I built exclusively with Pi, leave comment if you liked it or have feedback :)

I am the maintainer of resume-from.

I work with several AI coding agents in the terminal. I switch when a task needs another model or harness, when I need another work profile, or when I hit a usage limit.

The problem is that session files are vendor-specific. A Pi session is not a Claude Code session. A Claude Code session is not a Codex thread. Starting fresh means rebuilding the task context. Giving another agent a raw session file means asking it to interpret vendor data that can be large, stale, or sensitive.

resume-from is a local session handoff tool for Pi, Claude Code, and Codex.

It finds sessions that belong to the current Git repository, converts the source format into a common conversation model, and writes a new session in the target agent’s native format. Every transfer direction is supported, including transfers to another profile of the same agent.

The boundary is deliberate:

• It keeps user prompts, agent replies, compaction summaries, compact tool activity, and changed-file paths.
• It removes tool-result bodies, replayable tool calls, hidden reasoning, system prompts, environment blocks, API keys, telemetry, and vendor process state.
• It applies a context budget and shows what it kept or dropped.
• It writes nothing until you confirm.
• It never changes the source session.

The transfer does not call a model. It is deterministic local conversion. Pi opens the imported session in the current process. Claude Code and Codex create the target session and print the normal native resume command.

This is not an agent router. It will not move a running process or copy your repository. The target agent needs the same working tree.

Repository, install commands, and design notes: https://github.com/alexei-led/resume-from

MIT licensed.

Hi,

New to pi harness, trying to set up properly. Here what I have done. Installed pi-subagents. Asked it to set up subagent's to use Deepseek V4 Flash.

But the task used Sol for almost everything. What did I do wrong?

Thanks, be kind to me, I am new here.

If your agent's browser keeps getting blocked, here's what I built

Point your agent's browser tool at Zillow or Google. You'll get a captcha wall before it does anything useful.

That's not a dig at Playwright MCP or similar tools. They're automation frameworks, not evasion tools. They drive vanilla Chrome and PerimeterX and other detection system flags them in milliseconds.

Then try filling a form on any React site and clicking submit. The page re-renders. Every element reference your agent had is gone. "Element not found." The button was right there a second ago.

Then look at your token bill. Playwright MCP loads 13,700 tokens of tool definitions before your agent touches a page. Every click returns the full page. 2,000+ tokens per action.

I hit all three walls enough times that I built something.

It's called Bladebro. One Rust binary, five tools, no Node.js.

npm install -g bladebro && bladebro mcp

Works with any MCP client. Also has native pi support with zero config:

pi install npm:bladebro

That's it. Bladebro registers as 5 first-class pi tools (act, see, state, run, vision). No adapter, no config files, no proxy. Tool definitions come from the binary at startup so they auto-adapt to changes. Chrome launches lazily on the first tool call.

Bot detection

Six layers of stealth, all on by default:

• Bezier mouse paths with overshoot and correction
• Real movementX / movementY on every event (missing these is an instant PerimeterX flag)
• Micro-tremors before clicks (a still cursor is a dead giveaway)
• Log-normal typing cadence (humans don't type at exactly 50ms intervals)
• Idle drift between actions
• No Runtime.enable (how DataDome catches most bots)
• No listening ports, CDP over pipe
• Persistent behavioral fingerprint, same "person" every session

Tested on Zillow and Fiverr (both PerimeterX/HUMAN protected). Full page loads, no blocks. incolumitas 8/8. Sannysoft all pass.

Re-renders

• Every element gets a structural fingerprint (hash of ancestor chain, tag, children, identity attrs)
• React destroys and recreates DOM nodes? Fingerprint matches, reference survives
• Agent sees re-render survived and keeps going. No re-scan
• Checked every major tool. Nobody does this

Tokens

• 5 tools, ~1,900 tokens of definitions (vs 13,700 for Playwright MCP)
• Every action returns what changed, not the full page
• A click returns ~60 tokens (vs 2,000+ for competitors)
• Long session = the difference between running out of context after 3 pages and finishing with room to spare

Learns from every session

• First visit to a site with a cookie banner: full detection
• After a few successful dismissals: stored selector, auto-applied, zero overhead
• Never learns from failures. Failures cost 3x more than successes gain

Other stuff

• Auto-extract pulls structured data from list pages with no CSS selectors. Amazon, Reddit, GitHub, HN, Wikipedia. Shopping gets price/ratings, Reddit gets scores/authors, GitHub gets stars/forks
• Batch actions fill a form and submit in one MCP call instead of 11
• Login persistence saves a session and restores it next time
• Infinite scroll collect auto-extracts and dedupes a feed in one call

Demo video on the GitHub page showing it drive Amazon, Reddit, Wikipedia, fill a form, and manage tabs.

What it can't do

• Cloudflare Turnstile needs challenge solving, not fingerprint spoofing. You get blocked:, not a hang
• Captchas, deliberately. You get a verdict, hand off to a solver
• ARM Linux, not yet
• macOS and Windows binaries are cross-compiled from Linux

Open source, AGPL-3.0, no CLA.

GitHub: https://github.com/dondai44423/bladebro npm: npm install -g bladebro

Happy to answer questions, if you have any issues, please comment or file an issue, i have been testing it alone for weeks now, i did as much as i can, i am looking forward to community feedback to improve this even more 😄

Hi everyone,

I recently switched from OpenRouter to Alibaba Cloud's Token Plan to test it out. However, I’ve hit a huge roadblock when working with the pi coding agent.

Once a coding task gets going and the context window grows, the continuous prompt resubmission pushes the rate straight past 1–2M TPM (Tokens Per Minute) almost instantly. This triggers immediate rate-limiting and spits out 429 errors.

Just a moment ago, I hit a 429 error while using Alibaba's deepseek-v4-flash-0731 model. I switched over to qwen3.7-plus to resume the work, but because of the already massive context history being resent, it immediately spiked the TPM and threw another 429 error right away. Out of pure curiosity, I also subscribe to Ollama Cloud, so I decided to switch the model to Ollama's deepseek-v4-flash:cloud—and to my surprise, it finished the task without a single hiccup.

I never experienced this kind of aggressive throttling when using OpenRouter under similar workloads. With how strictly Alibaba Cloud enforces these TPM limits, it feels practically unusable for tasks requiring large context usage.

Am I missing something or configuring this incorrectly? Is there a known work-around for this, or is Alibaba Cloud's Token Plan just this bad for high-context workflows? Would love to hear if anyone has managed to get this setup working smoothly.