All Things Open 2026 – Session Recommendations

All Things Open 2026 is on in Raleigh! With more than 100 sessions and speakers, choosing what to attend is not easy.

I went through the conference program and shortlisted the sessions that caught my attention. My interests lean toward AI agents, software architecture, developer productivity, security, and data engineering, practical engineering challenges, along with a few interesting topics on model training, real-time AI, and emerging user interfaces.

AI coding assistants have evolved considerably. We’re now moving toward autonomous agents that can modify repositories, execute commands, provision infrastructure, and even deploy applications. But giving an agent the ability to write code is relatively easy. Making it reliable, secure, efficient, and useful in a real engineering environment is a much harder problem.

AI Coding Agents and Developer Infrastructure

1. Death by Worktree: Why Agent Sandboxes Choke on Disk I/O

Speaker: Bradley Hilton

Why attend: Git worktrees and isolated sandboxes are becoming common approaches for running multiple coding agents concurrently. But filesystem performance can become a serious bottleneck. This session addresses an often-overlooked infrastructure problem: the I/O overhead associated with agent execution environments. Particularly relevant for anyone experimenting with parallel coding agents and multi-repository workflows.

2. Building Functional Dev Environments for Humans and Agents

Speaker: Matyas Danter — Staff Solutions Engineer, Coder

Why attend: AI agents need reproducible development environments just as much as human developers do. The talk explores practical approaches to managing development environments across monorepos, multiple repositories, and distributed teams.

3. AI Sandboxes: Running Coding Agents Safely in Production-Grade Environments

Speaker: Shivay Lamba — Lead ML Engineer, Qualcomm

Why attend: Coding agents execute commands and generate code that may not be entirely trustworthy. Sandboxing is becoming essential for safely scaling autonomous development.

4. Sandboxing AI Agents in the Enterprise: A Practitioner’s Guide to Isolation Technologies

Speaker: Balu Mathew — Machine Learning Engineering Technical Leader, Cisco

Why attend: An architectural perspective on isolation technologies for enterprise AI agents. I’m particularly interested in understanding the security, performance, and operational trade-offs between different sandboxing approaches.

5. The Last Mile of AI-Assisted Development

Speakers: Colin Griffin and David Johnson — Krumware / SUSE

Why attend: Generating code is only part of software delivery. Building, testing, deploying, and maintaining it remain substantial challenges. The session includes a look at extending Rancher using Epinio, an open-source Kubernetes application deployment platform. Worth considering for its practical platform-engineering angle, although the presentation has a vendor-specific component.

Agent Security, Permissions, and Governance

6. You Can Give Any Agent Access to kubectl. But How Do You Make One That Should Have Access to kubectl?

Speaker: Charles Johnson — Mezmo

Why attend: Connecting an agent to Kubernetes is easy; deciding whether it should be trusted with cluster operations is not. This session challenges the assumption that giving agents write access to production systems must inevitably end in disaster. An interesting opportunity to explore controlled autonomy, operational safeguards, and the boundary between agent capabilities and permissions.

7. Fine-Grained Access Control for Hosted MCP Servers You Don’t Own

Speaker: Nick Taylor — Developer Advocate, Pomerium

Why attend: One of the more practical security topics on my list. Nick demonstrates an identity-aware proxy that enforces per-tool authorization across hosted MCP servers without modifying them. The approach addresses credential sprawl, OAuth limitations, and tool-call auditing. Nick has presented extensively on MCP and Zero Trust security, including at Black Hat and other technical conferences. The demo uses open-source Pomerium.

8. AI Wrote the Code – Is It Secure?

Speaker: Adhithi Ravichandran — Founder and Software Consultant, Surya Consulting

Why attend: AI-generated code can introduce vulnerabilities even when it appears functionally correct. This session examines the security implications of AI-assisted software development. Adhithi’s software consulting background should offer a practitioner perspective on the security checks and engineering discipline needed before trusting AI-generated code in production.

Model Training, Real-Time AI, and New Interfaces

9. The Beginners Guide to Training Your Own AI Coding Model on Your Own Data

Speaker: Bekah Hawrot Weigel — The Paper Compute Company

Why attend: I’m particularly interested in small, specialized coding models trained on custom datasets. Understanding how to prepare training data and adapt a model for specific coding tasks is becoming increasingly valuable. This should be a useful introduction for developers exploring model customization beyond prompting and RAG.

10. How Open is your Realtime AI Stack?

Speaker: Omar Warraky — Senior Solutions Architect, LiveKit

Why attend: Real-time voice AI requires considerably more than an LLM. Speech recognition, synthesis, media transport, orchestration, and latency management all matter. This session should be particularly interesting for understanding the open-source components and architectural choices available when building low-latency conversational applications.

11. Where Is My Futuristic App? Generative UI and the Open Future

Speakers: Mark Techson, Andrew Brogdon, and Marc Dougherty — Google

Why attend: What happens when agents generate not just content but the user interface itself? This keynote explores Generative UI and the A2UI protocol, including a look at a working application’s codebase. Although Google-led, the focus on open protocols and actual implementation makes it worth watching.

12. To Boldly Go: Data Science Exploration with AI Agents (ft Star Trek’s LCARS Theme)

Speaker: Dawn Wages — Director, Community and Developer Relations, Anaconda

Why attend: A creative approach to exploring data science through AI agents, using Star Trek’s LCARS interface as inspiration. This looks like an enjoyable opportunity to see how conversational agents and interactive interfaces can make data exploration more approachable.

Software Engineering and the Developer Community

13. When Code Costs Nothing: Tech Debt in the Age of GenAI

Speaker: Rachel Stephens — Research Director, RedMonk

Why attend: AI dramatically reduces the effort required to produce code, but does it reduce the long-term cost of maintaining software? This session examines how generative AI changes our understanding of technical debt, code ownership, and engineering accountability. This session should bring an independent industry-research perspective, making this particularly appealing as a counterbalance to AI productivity hype.

14. AI Tinkerers Raleigh: Top Demos of the Year and Their Community Impact

Speaker: Danny Castro — AI Tinkerers Raleigh

Why attend: Sometimes the most interesting innovations come from local builders rather than major technology companies. This demo showcase highlights practical AI projects from the Raleigh community. A good opportunity to discover emerging tools, meet developers, and see what people are actually building.

Final Thoughts

Looking across these sessions, a few important themes stand out:

  • Agent infrastructure matters. Sandboxing, and reproducible environments are becoming fundamental building blocks of AI-assisted development.
  • Security cannot depend on trusting the model. Permission boundaries, isolation, authorization, and auditing must be enforced outside the agent itself.
  • The software engineering bottleneck is shifting. Generating code is becoming easier, while validation, maintainability, and production readiness remain difficult.

See the complete All Things Open Tuesday schedule for session times and locations.

Looking forward to seeing what the speakers and the open-source community have been working on!

Who needs Surface Ultra RTX?

Microsoft announced the new Surface Ultra RTX Laptop today. Of course I want the RTX Spark Machine! Who doesn’t? It is awesome! I am being nostalgic about my original Microsoft Surface Pro from 2013 though. I have fond memories of developing and testing Windows 8 Apps and Windows Phone Apps (remember those?) on it.

The spec sheet of my Surface Pro 1 from 2013 is ancient: 3rd-gen Intel Core i5, 4 GB of RAM, 128 GB SSD. Windows support ended years ago.

I decided to wipe it and give it a new life with xubuntu.

xubuntu 26.04 refused to boot at first. The culprit turned out to be the installation media rather than Linux: the Surface was hitting USB enumeration and read errors with my original flash drive. Safe graphics made no difference. A different stick, USB 2.0 this time, dropped me straight into the live environment. Live environment was extremely choppy and slow. I was able to complete some basic validations. I settled on xubuntu 24.04 LTS for the permanent install, and I will revisit 26.04 now that the basic setup works.

Then the machine turned out to be more usable than I expected. Wi-Fi, Type Cover, and the Touch Screen all worked, and Firefox, Git, Python, terminals, SSH and lightweight dev tooling all run fine. XFCE and Ubuntu’s package ecosystem give me a current Linux environment that still leaves room to work in 4 GB of RAM. The Surface Pro’s compact form factor makes it light and portable next to my Lenovo Legion behemoth. The whole Surface Pro is probably lighter than the Legion’s power brick.

I am not going to run local LLMs, a Kubernetes cluster or a dozen containers on it. As a small Linux terminal, SSH client, coding machine, browser or home-lab console, it has plenty of life left.

I will be using it as a host for coding agents. The model runs in another local headless server (more on that in another blog post) or in the cloud, so what is left on this machine is an orchestration loop, HTTP calls, a git checkout and a shell, over tailscale. That is an I/O problem, and a 2013 i5 should do just fine. Headless over SSH, 4 GB should be enough. I’ll find out. The other appeal is blast radius. An agent that runs shell commands gets a cheap, disposable machine of its own instead of my main one.

There is something satisfying about taking hardware at the end of its commercial life and saving it from landfill.

Agentic finetuning using a custom harness

The goal was to figure out whether a small model running on my own hardware could do the job of intent classification and structured parameter extraction  well enough for one of my use-cases – read a request like “add 8 spare tent stakes to the camping box” and turn it into something the application can act on.

I created an agentic fine-tuning and evaluation loop with a custom evaluation harness: an AI agent proposes one change at a time, a custom evaluation harness measures it against a frozen test set, and every run gets appended to a log with its settings, result and next decision. The agent does the all the work of dataset generation, training, evaluation and diagnosis.

Setup

I built a fixed test set of 45 requests covering nine intents, five cases each, and froze it. Every model, change and training run was measured against those same 45 cases. The output metrics of each run were: intent accuracy, meaning did it pick the right action, and parameter accuracy, meaning did it extract the right details. A third important number is: fallbacks, the count of cases where the model declined to answer and returned “unclear.”

The OpenAI baseline (gpt-5.6-luna) was created and frozen. It scored 93.33% intent and 71.11% parameter accuracy with no fallbacks.

All local training ran on a consumer RTX 3060, in WSL2 with CUDA. LoRA fine-tuning, rank 16, alpha 32, learning rate 1e-4, batch size 4. A 15-epoch run takes about 20 minutes.

The entire source, including training data, eval harness, scripts and agent skill is on GitHub – needle-tuner.

Part 1 – needle2

The first local comparison with needle2 model from Cactus. It scored 4.44% intent accuracy. The problem turned out to be how I was asking the question. I had given the model one abstract, generic “classify this” tool. I replaced it with eight concrete tools, one for each real action the application supports.

The result went to 62.22% intent and 68.89% parameter accuracy.

With this reasonable starting point, I fine-tuned on a small purpose-built training set and ran a structured loop: change one thing, measure against the frozen test set, write down the result.

Needle2 configuration Intent Parameter
Base model, redesigned tools, no training 62.22% 68.89%
Best fine-tuned run (5 epochs, rank 8) 64.44% 62.22%
Longer training (30 epochs) 57.78% 64.44%
Heavier defaults 42.22% 51.11%

The best training result gained two points on intent accuracy over no training at all, and lost six on parameters. More training made things worse. The heaviest run also refused to answer far more often, with 15 fallbacks against 9 for the untrained model. The results were noisy enough to make any single comparison suspect. Two runs of the identical best configuration measured 64.44% and 60.00%, so I stopped treating one good run as evidence of anything. Validation loss kept improving while held-out accuracy got worse. Had I watched the training dashboard instead of the frozen test set, I would have shipped a regression. I stopped at this point.

Part 2 – needle3

A new version of the model family had shipped in the mean time, with a different architecture and a different native engine. Untrained, needle3 measured 71.11% intent and 66.67% parameter accuracy with 7 fallbacks. I modified the agentic loop to support the new model and executed again.

A one-epoch smoke test scored 68.89% and 73.33%, which is one run on one epoch and proves nothing on its own. A full 30-epoch run at the wrapper defaults scored 71.11% and 66.67%, identical to the untrained base. However, the per-intent breakdown had shifted underneath, some intents up and some down, while the totals stayed flat. Validation loss bottomed out at epoch 13 and climbed for the remaining 17 epochs while training loss kept falling. Same overfitting pattern needle2 had shown.

The issue

Instead of tuning another hyperparameter, I stopped reading aggregate scores and started reading individual failures.

The training data and the held-out test set agreed on which intent was correct. They disagreed on the convention for writing the parameters down.

The first problem was descriptive adjectives. For delete requests, the test set strips condition words from item names, so “the broken toaster” is expected to yield ‘toaster’. All 12 authored delete rows in my training data did the opposite, keeping ‘cracked mixing bowl’ and ‘expired pain reliever’ intact.

The second problem was a wrong key name. For inventory-viewing requests, the training data used a ‘location’ key for the container slot while every other intent, and the test set, used ‘boxLabel’. It also carried a ‘scope’ key that appears in zero held-out cases.

The model was learning my convention faithfully and being marked wrong for it, on 100% of those cases. That also explains why longer training hurt: more epochs meant learning the wrong answer more thoroughly.

The Fix

I corrected 12 delete rows and 20 view-inventory rows, regenerated the derived datasets, re-pinned the integrity hashes and re-ran the validation suite. No hyperparameter change and dropped Epoch count from 30 to 15, taken from the previous run’s validation-loss minimum.

Result: 82.22% intent and 75.56% parameter accuracy, 6 fallbacks.

Run Intent Parameter Fallbacks
Needle2 base 62.22% 68.89% 9
Needle2 best fine-tune 64.44% 62.22% not recorded
Needle3 base 71.11% 66.67% 7
Needle3 fine-tuned, original data, 30 epochs 71.11% 66.67% 6
Needle3 fine-tuned, corrected data, 15 epochs 82.22% 75.56% 6
OpenAI (frozen baseline) 93.33% 71.11% 0

That is 11 points of intent accuracy over its own base model and 20 over needle2‘s base. It is also the first time a local model beat the hosted API on parameter accuracy, 75.56% against 71.11%. Intent accuracy is still 11 points behind.

The per-intent movement matches the diagnosis. Delete went from 80%/40% to 100%/80%. Search went from 60%/60% to 100%/100%. Update improved as well, since the bare-noun convention is shared by every intent that names an item.

Validation loss behaved differently too. With the corrected data it declined every one of the 15 epochs, with none of the mid-run turnaround the unfixed run showed. The contradictory labels had been generating the overfitting signal themselves.

Next

Parameter extraction already beats the API. Intent classification is 11 points behind, and most of the remaining gap sits in one diagnosed failure mode: deciding whether a vague request is actionable.

The next lever is contrastive training examples sitting right on that boundary. After that the options get expensive: changes to how the model produces its answers, training that explicitly teaches it what a wrong answer looks like, or a different architecture.

Hyperparameter tuning and the size of the training dataset should also be revisited to explore further improvements to intent classification.

Because the same 45 cases were used to decide which experiment to run next, they are now a model-selection set rather than an unbiased final exam. Before anyone calls this production-ready it has to be tested against a separate set of cases it has never influenced.

The project also produced a reproducible pipeline along the way. A frozen test set, hash-pinned datasets, a documented training process, a log of every attempt including the failures, and a default path that validates without training and without calling paid APIs. That infrastructure is the reason the annotation issue was discoverable at all.

Giving OpenClaw Secure Access to Cloud Services Without Sharing Your Password

Device Code Flow has been built into OAuth2 for years, originally designed for TVs and game consoles. It works just as well for a Docker container. It requires no credentials stored on the agent machine. It gives you narrow, revocable access that the agent cannot exceed.

I have OpenClaw deployed in a sandboxed docker container on a dedicated host. I communicate with it via a Telegram bot, in a locked-down chat.

I wired the device-code authentication pattern up with OpenClaw and Microsoft (Personal/Consumer) services, but the authentication approach works with any app and any command-line tool you want to run in a headless environment, including Custom APIs. Device code flow + Entra ID app registrations give you a zero-stored-credentials gateway to any HTTP API you can build, not just Microsoft Graph.

What Is Device Code Flow?

OAuth2 device code flow (RFC 8628) was designed for “input-constrained devices” — things without a keyboard or a browser. Think of how you sign in to Netflix on a smart TV: a short code appears on screen, you visit a URL on your phone, you type the code in, and the TV logs in. You never type your password on the TV.

The official name for that pattern is the device authorization grant. A Docker container is, from the protocol’s perspective, exactly the same kind of device. It has no browser. It cannot perform an interactive redirect. But it can make HTTP requests, and that is all it needs.

The flow has three steps:

1. The app posts to the identity provider’s device code endpoint. It gets back a short user code (like “WDJB-MJHT”), a verification URL, and a polling device code.

2. The user visits the URL on any browser, on any device — their phone, laptop, anything — and enters the short code. They sign in normally, with their usual credentials and MFA if it is enabled.

3. The app polls the token endpoint in the background. Once the user finishes signing in, the poll returns a real access token and a refresh token. The app stores these and uses them for API calls going forward.

The app never sees the password. The password goes directly from the user’s browser to the identity provider. The app only ever handles two things: a short temporary code that it sends to the user, and a token that the identity provider gives back once the user has authenticated.

OpenClaw running in a Docker container fits this model exactly.

Which Services Support this?

Device code flow is not a Microsoft-only feature. It is a standard OAuth2 extension (RFC 8628) and most major identity providers support it — including Microsoft, Google, GitHub, and AWS. If a service uses Okta or Auth0 for identity, those support it too.

The Security Architecture

The agent never sees your credentials. Your password is typed in your browser, on your device, to your identity provider’s servers. It never touches the container. The agent only handles a short temporary code to give you, and a token issued by the provider once you have authenticated.

The device code is one-time and short-lived. After the user authenticates, the code is permanently invalidated. An intercepted code is useless without the user’s credentials and MFA.

Scopes define the ceiling. You configure the OAuth app or app registration to request only specific permissions. The agent cannot exceed those scopes. If you configure read-only access to email, the token cannot be used to send or delete email.

This is the authentication pattern that many of the consumer devices you already own use to access your accounts on your behalf. Your TV’s YouTube app, your smart home hub’s Google integration, the GitHub CLI you use on your workstation — these all use device flow. You have been trusting it for years without knowing what it was called.

How the implementation works

There are three components, and they run as separate Docker services that talk to each other through the Docker socket.

The app sidecar is your application container. It runs your CLI tool and your auth logic. It does nothing on its own. Its only job is to hold the authenticated token cache and execute commands on demand when OpenClaw calls into it.

OpenClaw is the AI agent container. It connects to Telegram, understands natural language, and knows which skills to run for which requests. It does not know anything about OAuth or your specific app. It just runs shell commands you specified and reports results back to you.

The Telegram auth bridge is a small script that lives inside the sidecar. It registers a device code callback, requests an auth flow, and uses the Telegram Bot API to forward the code to you — then confirms when authentication completes.

The compose file looks roughly like this:

```yaml
services:
  myapp-sidecar:
    image: ghcr.io/youruser/myapp:latest
    command: ["sleep", "infinity"]
    environment:
      - XDG_DATA_HOME=/data
      - TELEGRAM_BOT_TOKEN=${TELEGRAM_BOT_TOKEN}
      - TELEGRAM_CHAT_ID=${TELEGRAM_CHAT_ID}
    volumes:
      - ${APP_DATA_DIR:-./app-data}:/data
    restart: unless-stopped

  openclaw-gateway:
    build:
      context: .
      dockerfile: Dockerfile.openclaw
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
      - ./skills:/app/skills
    group_add:
      - "${DOCKER_GID:-999}"
    environment:
      - TELEGRAM_BOT_TOKEN=${TELEGRAM_BOT_TOKEN}
    ports:
      - "18789:18789"
    restart: unless-stopped
```

The Callback Pattern

The auth manager has a method called set_device_code_callback. You pass it a function, and when the device code is ready, the auth manager calls your function with the code and the verification URL rather than trying to open a browser.

```python
class AuthManager:
    def __init__(self, client_id: str, authority: str) -> None:
        self._client_id = client_id
        self._authority = authority.rstrip("/")
        self._on_device_code = None
        self._tokens = self._load_cache()

    def set_device_code_callback(self, fn) -> None:
        self._on_device_code = fn

    async def _device_code_auth(self) -> None:
        async with httpx.AsyncClient() as client:
            resp = await client.post(
                f"{self._authority}/oauth2/v2.0/devicecode",
                data={"client_id": self._client_id, "scope": OAUTH_SCOPES},
                timeout=30,
            )
        flow = resp.json()
        user_code = flow["user_code"]
        verification_uri = flow["verification_uri"]
        device_code = flow["device_code"]
        interval = flow.get("interval", 5)
        expires_in = flow.get("expires_in", 300)

        if self._on_device_code:
            self._on_device_code({
                "user_code": user_code,
                "verification_uri": verification_uri,
                "message": flow.get("message", ""),
            })
        else:
            try:
                webbrowser.open(verification_uri)
            except Exception:
                pass

        deadline = time.time() + expires_in
        while time.time() < deadline:
            await asyncio.sleep(interval)
            async with httpx.AsyncClient() as client:
                resp = await client.post(
                    f"{self._authority}/oauth2/v2.0/token",
                    data={
                        "client_id": self._client_id,
                        "grant_type": "urn:ietf:params:oauth:grant-type:device_code",
                        "device_code": device_code,
                    },
                    timeout=30,
                )
            body = resp.json()
            if resp.status_code == 200 and "access_token" in body:
                self._store_tokens(body)
                return
            error = body.get("error", "")
            if error == "authorization_pending":
                continue
            elif error == "slow_down":
                interval += 5
            elif error in ("authorization_declined", "expired_token"):
                raise AuthError(error)
            else:
                raise AuthError(body.get("error_description", "Auth failed"))
```

The polling loop handles the three error codes the spec defines: authorization_pending (keep waiting), slow_down (back off and increase the interval by 5 seconds), and the terminal errors that stop polling.

The Telegram Bridge

With the callback mechanism in place, the Telegram bridge is just a script that registers a callback and uses the Telegram Bot API to deliver the code to you:

```python
import asyncio, os, sys, traceback
import httpx
from your_app.auth import AuthManager
from your_app.config import Settings

TELEGRAM_BOT_TOKEN = os.environ["TELEGRAM_BOT_TOKEN"]
TELEGRAM_CHAT_ID = os.environ["TELEGRAM_CHAT_ID"]

async def send_telegram(text: str) -> None:
    url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage"
    async with httpx.AsyncClient() as client:
        await client.post(url, json={
            "chat_id": TELEGRAM_CHAT_ID,
            "text": text,
            "parse_mode": "Markdown",
        }, timeout=30)

async def main() -> int:
    settings = Settings()
    auth = AuthManager(settings.client_id, settings.authority)

    if auth.is_signed_in():
        await send_telegram("Already authenticated.")
        return 0

    def on_device_code(info: dict) -> None:
        msg = (
            "*Auth Required*\n\n"
            f"Go to: {info['verification_uri']}\n"
            f"Enter code: `{info['user_code']}`\n\n"
            "Complete sign-in in your browser and I'll confirm when done."
        )
        try:
            asyncio.get_event_loop().create_task(send_telegram(msg))
        except RuntimeError:
            import requests
            requests.post(
                f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/sendMessage",
                json={"chat_id": TELEGRAM_CHAT_ID, "text": msg, "parse_mode": "Markdown"},
                timeout=10,
            )

    auth.set_device_code_callback(on_device_code)
    try:
        user_info = await auth.sign_in()
        await send_telegram(f"*Signed in* as {user_info['name']} ({user_info['email']})")
        return 0
    except Exception as exc:
        await send_telegram(f"*Auth failed:* {exc}\n```{traceback.format_exc()[:800]}```")
        return 2

if __name__ == "__main__":
    sys.exit(asyncio.run(main()))
```

The OpenClaw Skill

On the OpenClaw side, a “skill” is a markdown file. When OpenClaw sees a trigger phrase in Telegram, it runs the associated shell command. The markdown file looks like this:

—
name: myapp-auth
description: Authenticate with the cloud service via device code flow, coordinated through Telegram.
—

## myapp-auth

Use this skill when the user sends “/auth”, “authenticate”, or “sign in”.

Run the following command and wait up to 360 seconds for it to complete.
The script sends the device code to the user via Telegram and confirms when done.

docker exec myapp-sidecar-1 python /app/scripts/telegram_auth.py

Summary

Device code flow is a mature, widely-supported OAuth2 pattern that maps naturally onto OpenClaw running in Docker container. With this pattern OpenClaw never handles your credentials. It gets scoped, revocable tokens from the identity provider, after you have authenticated in your own browser. A Telegram bot is all you need to coordinate the handoff of the temporary code.

The approach works for many use-cases across Microsoft accounts, Google, GitHub, AWS, Auth0, Okta and others. It supports exactly the kinds of personal automations that make an OpenClaw genuinely useful in daily life.

References

OpenflowSight – Log Analysis for Snowflake Openflow Telemetry

OpenflowSight is a Streamlit application for searching, filtering, and analyzing Openflow telemetry data, inspired by Azure App Insights Search UX.

It’s a log explorer focused on Openflow telemetry. It helps you quickly identify relevant events, see when they spiked, and which processors were involved. Search, group similar events, and export results for deeper offline analysis.

Openflow is an exciting new unified data integration tool from Snowflake based on Apache NiFi. Logs, traces, and metrics emitted at runtime are written to the Event table which supports the OpenTelemetry data model. OpenflowSight surfaces this data to enable convenient, efficient monitoring and analysis of Openflow operations from a graphical dashboard—instead of using SQL queries.

Key Features

  • Runtime Filtering — Select multiple runtimes, toggle system runtimes, filter by processor and log level
  • Smart Search — Multi-term search (comma-separated) with contains, regex, and exact match modes
  • Timeline View — Interactive histogram with zoom/pan, configurable time buckets (1 min to 1 hour), preset windows (1h/6h/24h/7d)
  • Grouped Patterns — Fuzzy clustering that normalizes dynamic values (timestamps, UUIDs) for accurate pattern grouping
  • Individual Logs — Excel-like grid with sorting, filtering, pagination, and multi-select
  • CSV Export — One-click export with smart file naming

Tech Stack

Open Source

OpenflowSight is open-source and contributions are welcome—features, fixes, docs, anything. If it helps you debug Openflow pipelines faster, that’s a win.

View on GitHub →

Understanding RAG @ All Things Open AI 2025

What I learned while creating Bookshelf (open source RAG Application)

I had the opportunity to speak at the AllThingsOpen.ai 2025 Conference recently. It was a wonderful experience to learn from and share with so many amazing fellow presenters and attendees. All Things Open AI 2025 session recordings are available now.

I spoke about several key aspects of Retrieval-Augmented Generation (RAG) and vector databases. The main topics I addressed included The Core Concepts of RAG, Embedding Models vs. Inference Models, Handling Multiple Embeddings and various Optimizations Beyond Naive RAG such as chunking strategies, enrichment with metadata, auto-merging retriever, and reranking models. Finally, I touched on Local Execution and GPU Utilization.

You can watch the recording here: YouTube – How I created Bookshelf: An Open Source AI-Powered Personal Knowledge Base – Ash Tewari

The slides can be downloaded from here: (Google Drive Link)

References and Links: