AI Ping-Pong with Polly (and Omnigent)

Sometimes the most entertaining way to test a new orchestration tool isn't to run a standard benchmark, but to lock two state-of-the-art AI models in a virtual room and make them debate historical facts. This week, I got to do exactly that using an interesting new tool called polly within the omniagent ecosystem.

The idea was simple: set up a relay where Claude and Gemini (specifically the Antigravity agent) pass a task back and forth, validating and revalidating each other's work.

However, getting there wasn't entirely smooth sailing. I see recent polly changes that seem to be working on Antigravity support but I presume that it is just heavily in beta and under active development - Simply because I couldn't get it to work cleanly on my local test setup out of the box. In a somewhat ironic twist of AI-assisted development, I ended up having Claude help me debug my environment and write the custom harness around Antigravity to get the toolchain working.

Once the harness was solid, the fun began. I decided to test their interaction on a simple prompt asking for a list of India's Prime Ministers (based on their first stint). I then pass that list back-n-forth between the two AI engines for cross-validation and updation. To avoid an endless loop, I limited the number of iterations to exactly two.

Here is the exact prompt and how it kicked off:

 ❯ ask antigravity to generate the list of prime ministers of india in the
 chronological order (of their first stint) and then pass the list to
 claude for validation and an updated list of the same. and pass that onto
 to antigravity for revalidation with the same request. Do this round trip
 only 2 times and display the resultant list.

   · thinking…
◆ I'll run this as a relay: antigravity generates → 
    Claude validates/updates → antigravity revalidates, repeated for 
    2 round trips. Starting with antigravity's initial list.

◆ Antigravity produced the initial list (15 entries). Round trip 1 —
   passing to Claude for validation + an updated list:

◆ Claude is validating now. Waiting for its result.

.
.
.

Watching the two models interact was fascinating. Antigravity would confidently output the initial sequence, Claude would meticulously review it (often catching nuance around interim Prime Ministers like Gulzarilal Nanda), and then Antigravity would process Claude's corrections. It functioned like a highly pedantic game of ping-pong.

While this specific test was just good fun, the real motivation behind integrating polly is much more practical. I've been working on a complex PostgreSQL B-tree index compaction patch to solve the index bloat problem that VACUUM leaves behind. Developing database internals is notoriously tricky, and I've been relying on Claude (and now Antigravity) to help write and re-write and review the code heavily.

Having two AI agents look at the developing patch from different angles has been invaluable. They continually find subtle bugs, naming inconsistencies, edge-case production gotchas, and deviations from C best practices. Instead of manually passing code snippets back and forth between different chat windows, the idea here is to see if polly (or a similar tool) can allow me to seamlessly automate this cross-validation part without my intervention.

Now that the Antigravity harness is patched up and working, I've proven that this dual-engine review process can work, and be an automated part of my Postgres hacking workflow. That said, because Google's API billing operates separately from the standard Google AI Pro account, this specific Claude-and-Gemini pairing was mostly a test run. For the near future, I may just end up cross-validating between Claude and Codex (or a similar alternative) to keep the pipeline running smoothly and cost-effectively.

For the curious, here is the Relay trace of that conversation, and the final result.

   Relay summary

    Hop               Agent                 Result
    ─────────────────────────────────────────────────────
    Generate          antigravity (Gemini)  15-entry list
    RT1 → validate    Claude                two changes
    RT1 → revalidate  antigravity           no changes
    RT2 → validate    Claude                no changes
    RT2 → revalidate  antigravity           no changes


   Resultant list — Prime Ministers of India (chronological by start of first term)


     1 Jawaharlal Nehru — 1947
     2 Gulzarilal Nanda (Acting) — 1964
     3 Lal Bahadur Shastri — 1964
     4 Indira Gandhi — 1966
     5 Morarji Desai — 1977
     6 Charan Singh — 1979
     7 Rajiv Gandhi — 1984
     8 Vishwanath Pratap Singh — 1989
     9 Chandra Shekhar — 1990
    10 P. V. Narasimha Rao — 1991
    11 Atal Bihari Vajpayee — 1996
    12 H. D. Deve Gowda — 1996
    13 Inder Kumar Gujral — 1997
    14 Manmohan Singh — 2004
    15 Narendra Modi — 2014

   One note worth flagging (the cross-checks agreed, but for completeness): this
   list counts Gulzarilal Nanda once at his first acting stint (1964). He also
   served a second acting term in 1966 after Shastri's death — both models
   consistently treated 'first stint' as the ordering key, so he
   appears a single time at 1964, which matches your request.
 

Keep having fun !!

Taming the Token Burn

Cloud AI is incredibly convenient, but the costs scale rapidly once you start leaning on long-context workflows. I've spent the last year relying on Gemini Pro for everything from Postgres automation to complex scripting, but the honeymoon phase is officially over (for me). As my projects grew in scope, so did the token consumption, eventually hitting a brick wall that forced a rethink of my entire stack.

For most, this is a niche problem. For anyone building high-context agentic workflows, it’s the only problem that matters.

Last year, the ride was smooth. My "AI usage" was minimal and Gemini Pro handled my daily tasks with ease. However, over the last three months, I've started pushing it with "decent-sized" projects—tasks that require keeping dozens of context balls in the air simultaneously. The result? It guzzles through tokens like a V8 engine in a traffic jam.

The breaking point came recently when, during a deep-dive development session, I fed the model a substantial task involving a complex codebase. Within two hours, I burned through 100% of my monthly allocation!!

Three (3) months in a row!

Yeah, that's not going to fly.

Finding the Right Tool for the AI Job

It’s time for a pivot, but not necessarily a total exit from the cloud. I’ve recently taken a Claude subscription as well, and it has been proving to be remarkably good at tasks where Gemini used to stutter. It’s basically been a lesson in finding "the right tool for the job" rather than looking for a single silver bullet.

That said, reducing my reliance on expensive cloud tokens is a priority. The recent NVIDIA RTX 5060 Ti purchase was a steep investment, but nonetheless a strategic move towards local autonomy. The goal is to migrate my heavy-lift agentic AI workflows to my local server farm, reserving premium cloud services for the few tasks where they are truly indispensable—specifically, massive context shifts that can't be easily decomposed. Some key examples being planning complex projects, or tasks that require reasoning over large codebases.

I have a few machines at home (hosting IronClaw, Postgres fuzzing, Jellyfin, Git servers, Obsidian repositories, and a Grafana dashboard to keep an eye on it all), and the goal is to keep costs down while making these tools smarter. The only way I see that happening is if I reduce Cloud API dependence and switch existing tooling to local LLMs.

Another key driver is that I expect some of my newer projects, still in stealth mode, to burn through tokens like nobody's business; they wouldn't see the light of day if I didn't change the status quo.

Stay tuned.

Display IMDb Ratings on Einthusan

Surfing niche streaming sites without inline film ratings is a recipe for endless tab-opening and "analysis paralysis." To scratch my own itch, I put together a small userscript called Masala Script to fix this exact problem.

For context, Einthusan is a massive streaming directory for South Asian cinema. While it's an excellent digital archive, exploring its thousands of regional films is tedious because it lacks external metadata like IMDb ratings.

To solve this friction, Masala Script (presently just a single Tampermonkey extension file) reads the Einthusan page, interfaces with the free OMDB API, and renders IMDb rating badges right next to the title.

Technical Features

While it might seem trivial to inject an API call onto a DOM load, building this script properly required addressing a few interesting technical hurdles:

• Intelligent Fallback Matching via Wikipedia: South Asian movie titles vary wildly in transliteration. An exact-title search against the OMDB API frequently fails for regional films. However, Einthusan usually provides a Wikipedia link for each movie. I built a transparent scrape fallback: if the direct title/year fetch fails, the script fetches the linked Wikipedia page in the background, extracts the definitive ttXXXXXXX IMDb ID using a regular expression, and then repeats the OMDB query using that exact ID for 100% accuracy.
• Aggressive Client-Side Local Caching: The free tier of the OMDB API provides 1,000 requests per day. A typical Einthusan browse page can render over 20 movie cards at once. Scrolling through just 50 pages would immediately exhaust the daily allowance and result in rate limits. The script counters this by heavily utilizing the GM_setValue and GM_getValue Tampermonkey APIs—caching successful queries in the browser for 7 days, and failed title lookups for 1 day.
• Detailed Error Tooltips: Rather than failing silently, any lookup that ultimately misses (or fails due to API config errors) renders a "Fail" or "N/A" UI badge. When hovered, it provides an exact exception traceback or error string so the user knows exactly why the movie metadata wasn't found.
• Single Page Application Navigation Detection: Einthusan manages categories via AJAX updates, utilizing history.pushState and popstate to load new frames. The userscript actively monkey-patches and listens to these navigation boundaries, injecting the DOM observers properly on dynamic content swapping.

The Genesis of Masala Script

Built using modern AI tools, this project demonstrates how rapidly useful and robust browser enhancements can be coded from scratch with minimal manual boilerplating.

Looking at the initial Git history over the course of the day shows how quickly the script escalated from a basic exact-title scraper into a much more mature and intelligent tool:

commit 70027efcd906586cb46928b6da16c46c2402ae25
docs: replace development instructions with a detailed features and notes section in README.

commit 133dbdf7e6123f3a9ac774d820e1a1aa932051f4
Add caching for imdb ratings

commit acb25cc1187884e771efa9e32cf9836461d403c1
feat: Do a fallback search (via Wikipedia), in case first imdb rating fetch fails.

commit 06f3f05be8f6ebd08dfedffb80cf4d53544786f6
feat: Add movie page to the list of pages where this works

Try It Out

If you want to try it out on your next Einthusan movie night:

1. Install the Tampermonkey browser extension.
2. Claim a free OMDB API Key.
3. Install the script by clicking on imdb-einthusan.user.js.

The next time you navigate to any browse or movie page on Einthusan, the script will prompt you for your OMDB API key (only once), and start displaying ratings next to the movie cards!

Although this extension might only interest a very niche subset of users, for those of us who regularly browse Einthusan (and heavily rely on IMDb to filter through the noise), it's a massive quality-of-life add-on to have.

If anyone is looking for new features, has a bug to report, or wants to contribute, feel free to create an issue or submit a pull request on the repository. Happy watching!