Ranga Sampath | Network Expert | AI Things Builder
@youplusai
Building Agentic AI tools to solve high stakes problems in Cloud Networking.
The Network Ghost Agent encodes senior architect methodology into safe-exec code.
I went dark for three years. The podcast downloads didn't stop.
Post January 2022, I stopped the You+AI podcast โ it had reached Top 8 globally in its category. Full guest pipeline. Sent them all declines. Later, took an enterprise engineering role and went heads-down.
Didn't delete anything.
๐ก Eight months after stopping: still getting monthly downloads.
Twelve months. Twenty months.
Requests for new episodes and guest recommendations arriving in 2023, 2024, even into 2026.
In 2020 I had written about four things I believed made innovation work:
*Be Curious. Embrace Change. Ride the Wave. Partner.*
๐ What I found when I came back in 2026 โ to build open-source agentic AI tools for cloud network troubleshooting โ was that all four had kept running without me.
My website archive did the work I was not doing. The timestamped work log outlasted any portfolio I could have built. The name You+AI turned out to describe a thesis, not a topic: human expertise and AI capability working together, whoever the human is, whatever the domain.
The tools I am building now are the most honest expression of that thesis yet.
Dormant is not dead. The foundation is real, or it isn't. Stopping is not the same as failing.
Original 2020 recipe, built to last, read the full recipe @ https://t.co/4ygvyDAxatโ
#PersonalBrand #Innovation
Yes this is what I have realised over time ie it is best to keep context separate for unrelated tasks. I use Claude projects to separate the tasks. Each project gets its own knowledge base.
Related tasks can be different conversations in the same project if they did indeed share the knowledge base of files uploaded to the project.
Recently I experienced the non-determinism first hand when upgrading models.
What used to work for the customer no longer worked-- the underlying behaviour of model had changed. The behavioural contract of how models will work for your use case in your domain isn't spelled out anywhere.
Wrote about this -- https://t.co/kaVgUTs8wD
LLM Vendors tell you about ๐ฐ๐ผ๐ป๐๐ฒ๐ ๐ ๐๐ถ๐ป๐ฑ๐ผ๐ ๐๐ถ๐๐ฒ๐ and ๐ฏ๐ฒ๐ป๐ฐ๐ต๐บ๐ฎ๐ฟ๐ธ ๐๐ฐ๐ผ๐ฟ๐ฒ๐
They cannot tell you what the model actually does on your infrastructure.
..
..
That gap is the ๐ฃ๐ฆ๐ฉ๐ข๐ท๐ช๐ฐ๐ณ๐ข๐ญ ๐ค๐ฐ๐ฏ๐ต๐ณ๐ข๐ค๐ต โ and it's invisible until you deploy.
๐ I ran 30 real network investigations (10 uses cases * 3 models) โ Gemini 2.5 Flash, Sonnet 4.6, Claude Haiku 4.5 โ with identical system prompts and fault scenarios. All three identified the root cause correctly. But here's what did not work out as expected:
๐ ๐ฅ๐ฒ๐บ๐ฒ๐ฑ๐ถ๐ฎ๐๐ถ๐ผ๐ป ๐ฑ๐ฒ๐ฝ๐๐ต: Does the model understand what the fix touches beyond the immediate problem? One model knows that `--service-endpoints` replaces the entire list and includes existing endpoints in the command. The other two omit them. *SQL connectivity fails silently.*
๐ง ๐ง๐ผ๐ผ๐น-๐๐ผ-๐ต๐๐ฝ๐ผ๐๐ต๐ฒ๐๐ถ๐ ๐ฎ๐ฐ๐ฐ๐๐ฟ๐ฎ๐ฐ๐: When the model forms a hypothesis about a specific layer โ OS firewall vs. traffic shaping vs. routing โ does it pick the right inspection tool? Or does it use a sender-side packet capture to verify a routing blackhole (spoiler: wrong tool, wrong answer)?
โ ๏ธ ๐๐ฎ๐๐ฒ๐น๐ถ๐ป๐ฒ ๐๐๐ฎ๐ด๐ฒ: If you provide reference data in the prompt, does the model use it? Or does it investigate from scratch and take 10x longer?
๐ก๏ธ ๐๐ถ๐ ๐๐ฎ๐ณ๐ฒ๐๐: Same diagnosis. Same confidence: high. Entirely different blast radius depending on whether the model understands what the command does to adjacent config.
They properties don't appear until you run the model on your actual domain, against your actual use cases.
๐ก I documented what each model does well, where it struggles, and how to build your own evaluation framework before you migrate production traffic. Because when the vendor deprecates a model, the migration guide covers API compatibility. It doesn't cover whether the replacement still knows to verify at the effective route table instead of a PCAP.
The behavioral contract you validated is valid for the model you validated it on. ๐ช๐ต๐ฒ๐ป ๐๐ต๐ฒ ๐บ๐ผ๐ฑ๐ฒ๐น ๐ฐ๐ต๐ฎ๐ป๐ด๐ฒ๐, ๐๐ต๐ฒ ๐ฐ๐ผ๐ป๐๐ฟ๐ฎ๐ฐ๐ ๐ฐ๐ต๐ฎ๐ป๐ด๐ฒ๐.
Read the full details @ https://t.co/jejh93xsbaโ
#AIBuilders #LLMModels
@GergelyOrosz Build a StackOverflow over LLM returned responses in the org ๐
Show the top 10 repeated queries over the hour in a dashboard visible to everyone.
Thank you for sharing @dharmesh -- insightful piece.
One problem that I've faced recently which related well with this article is what happens when you migrate to a newer and shinier model.
Things break and you need to solve it for the customer if you are building off the Yellow Brick Road. Wrote about my experience here -- https://t.co/kaVgUTs8wD
A specific problem that I can relate to with this article -- LLM model changes and the push by vendors to upgrade to the latest and shiniest model. Turns out you need to testiest and re-validate a lot of what worked before when the model changes.
This is what you need to do for the customer when you are building off the Yellow Brick Road. Wrote about it here -- https://t.co/kaVgUTs8wD
I swapped the AI model in a production network forensics agent.
The confidence scores didn't change. The fix recommendations did.
๐ 30 runs. 10 fault scenarios. Same prompt. Same Azure infrastructure. Three models:
Gemini 2.5 Flash, Sonnet 4.6, Haiku 4.5.
-- Overall diagnosis accuracy: 90%.
- -Remediation safety: 73%.
Every unsafe fix โ delivered at confidence: high.
โ ๏ธ The clearest example: a missing service endpoint. All three models found it. Two gave
a fix that would silently delete another service endpoint added during that morning's
maintenance window. One gave the safe command โ and explicitly warned that the CLI
argument is a full list replacement, not an append.
Same diagnosis. Same confidence score. Entirely different blast radius.
๐ก That's the behavioral contract โ the implicit agreement between your system prompt
and a specific model about how to reason and what a fix does to adjacent config.
That contract is NOT documented by any vendor.
When the model changes, the contract changes. The migration notice won't mention it.
I wrote this up in full โ 3 failure modes, what held cleanly across all three models,
and what a remediation safety eval actually looks like.
Check it out โก๏ธ https://t.co/kaVgUTrAH5