ODSC (Open Data Science Conference) AI engagement report
@_odsc - 111K followers on X
Measured over 48 original posts from a 30-day window, last computed on October 10, 2026.
Engagement
A typical post picks up 4 interactions against 111K followers, an engagement rate of 0.003%. Measured over 48 original posts, its engagement rate beats 10% of 21,185 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 619 times each, and 0.565% of those impressions turn into an interaction. That is about 0.56% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 37% of days saw any activity at all. Most posts go out around 23:00 UTC, and Thursday is the busiest day of the week. Of the 48 posts sampled, 60% carry an image or video, 17% are part of a thread and 83% link out. The account's strongest tracked post pulled 8 interactions, about 2.0x its own typical post. Recurring topics include #ai, #artificialintelligence, #datascience.
Measured over 48 original posts from a 30-day window, last computed on October 10, 2026. Recurring tags: #ai, #artificialintelligence, #datascience.
Compared with accounts its own size
ODSC (Open Data Science Conference) AI's engagement rate beats 10% of the tracked X accounts closest to it in follower count (21,185 accounts, accounts of similar size (decile 8 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.
On engagement per impression rather than per follower it beats 27% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.
Where this sits in the catalog
At 0.003%, ODSC (Open Data Science Conference) AI sits below the 10th percentile of the 212,567 accounts in this comparison. That places it in the bottom 25% band, which runs below 0.025%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.146% |
| 75th percentile | 0.696% |
| 90th percentile | 2.61% |
| 99th percentile | 82.7% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 23:00 UTC, and Thursday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 89K |
| 01:00 UTC | -2% | 90K |
| 02:00 UTC | -3% | 88K |
| 03:00 UTC | -4% | 94K |
| 04:00 UTC | -5% | 76K |
| 05:00 UTC | -4% | 75K |
| 06:00 UTC | -5% | 86K |
| 07:00 UTC | -5% | 93K |
| 08:00 UTC | -4% | 108K |
| 09:00 UTC | -4% | 124K |
| 10:00 UTC | -3% | 129K |
| 11:00 UTC | -3% | 141K |
| 12:00 UTC | -3% | 154K |
| 13:00 UTC | -3% | 167K |
| 14:00 UTC | -4% | 173K |
| 15:00 UTC | -2% | 176K |
| 16:00 UTC | -3% | 171K |
| 17:00 UTC | -3% | 159K |
| 18:00 UTC | -2% | 149K |
| 19:00 UTC | -2% | 141K |
| 20:00 UTC | -1% | 131K |
| 21:00 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
Formats this account uses
Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 60% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 83% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 60% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
- 83% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
- Its average post runs 540 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above the same accounts' other posts.
These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.
Best tweets
- Oct 3, 20262.0x their median
Explore this visual guide to AI agent architecture and understand the key components that power autonomous AI systems and agentic workflows. #DataScience #AI #ArtificialIntelligence #ODSC https://t.co/hULZUo7W2l
- Oct 1, 20262.0x their median
AI agents can only be truly autonomous if they have the authority to act and that authority is tightly controlled. At ODSC AI West 2026, @ianlivingstone, Co-Founder & CEO of @KeycardAI, will present “Autonomy Requires Authority: Building Auth for AI Agents.” Ian brings 15+ years of experience across developer tools, security infrastructure, and platform engineering, including previous roles at Snyk and Salesforce. At Keycard, his work focuses on trusted, autonomous agents and the infrastructure needed to let them access tools and services safely. In this session, he’ll explore the shift from static credentials and inherited permissions toward ephemeral access, delegated authority, runtime context, and attributable agent actions. Join Ian at ODSC AI West 2026: https://t.co/V6VhBRehp9
- Oct 6, 20261.8x their median
An AI agent can complete every tool call, trigger no errors, and still fail the customer. At ODSC AI West 2026, Dr. Diane Lin, Tech Lead at @datadoghq, will present “Detecting Silent Agent Failures Before Your Customers Do.” Diane leads product development on self-improving AI agents for security teams and brings experience across Datadog, Culminate, Zscaler, Amazon Alexa, and Vicarious. Her work focuses on detecting failures that traditional monitoring can miss. In this session, she’ll show how production traces can reveal anomalies in planning, tool use, recovery, and final answers and how those signals can feed back into evaluation, remediation, and real-time self-correction. Join this AI Engineering, Evals & AIOps talk at ODSC AI West 2026: https://t.co/LwfWcmfVyJ
- Oct 3, 20261.8x their median
Our interactive LLM Explainer shows what happens as a language model processes and generates text. See how text gets broken into tokens. Explore attention across layers and heads. Compare next-token probabilities. Then watch generation unfold token by token using Greedy, Top-K, and Top-P sampling. No code required. 🔗 Explore it: https://t.co/xI6s8w2v1t It’s a practical way to understand what’s happening underneath the prompt. And if you’re ready to move from exploring how LLMs work to building AI systems yourself, the ODSC AI Engineering Accelerator takes you further with hands-on, instructor-led training. Ready to build AI? 👉 Learn more and register: https://t.co/34ftzDaE42 #AIEngineering #LLM #GenerativeAI #MachineLearning #AIAgents #ODSC
- Oct 2, 20261.8x their median
Explore why adding AI to your team isn’t enough—and how rethinking workflows can help organizations turn AI tools into meaningful productivity gains. #DataScience #AI #ArtificialIntelligence #ODSC https://t.co/qIrVFmj2wY
- Oct 1, 20261.8x their median
Coding agents can generate data pipelines fast. Trusting those pipelines in production is a much harder problem. At ODSC AI West 2026, @MBroecheler, Founder of DataSQRL, will lead the training “Agentic Data Engineering You Can Trust.” Matthias brings deep experience across databases, graph systems, machine learning, and production data infrastructure. He previously served as Chief Technologist at DataStax, created TitanDB/JanusGraph, and co-authored The Practitioner’s Guide to Graph Data. In this session, attendees will explore how to constrain agent-generated data systems with deterministic compilation, validation, code analysis, and dataflow simulation, while keeping the architecture understandable through SQL and open-source infrastructure including Apache Iceberg, Kafka, Postgres, and Flink. Join this AI Engineering, Evals & AIOps training at ODSC AI West 2026: https://t.co/lsspc4Cv8u
- Sep 30, 20261.8x their median
The real shift in AI isn’t from worse models to better ones. It’s from prompting AI to delegating real work. @alexgraveley, co-creator of GitHub Copilot and now building UFO AI, explains why longer-running models, shared memory, parallel agents, and better computer use are changing how teams build software and run companies. He also gets into why engineers may spend less time writing code directly, how trust and permissions need to evolve, and why much smaller AI-first teams could take on work that once required far more people. “My default is to delegate.”
- Oct 10, 20261.5x their median
Coding agents can move fast. But if teams keep correcting the same mistakes, the system itself is not learning. At ODSC AI West 2026, @VDMatos, CEO of Iteration, will lead the training “How to Build a Software Factory: From Coding Agent Chaos to Compounding Improvement.” Valentin’s work focuses on turning recurring friction across coding, reviews, and team feedback into better instructions, checks, and workflows for agents, so improvements become part of the system instead of disappearing after each task. With experience as Gladia’s first employee and Lead AI Engineer, as well as roles at Entrepreneur First and Delphi, he brings a practical perspective on building production agent systems that improve through use. Join this training at ODSC AI West 2026: https://t.co/dxVut4ZYvc
- Oct 7, 20261.5x their median
“The log is the agent.” Yohei Nakajima, creator of BabyAGI, explains why long-running AI agents need more than memory: they need durable state, history, and a record they can resume from. That idea led to ActiveGraph, his runtime for agents that can recover after interruptions, preserve context, and make their actions inspectable. Also covered: workflow recovery, research reproducibility, and multi-model research systems.
- Oct 9, 2026
Your agent fails in production. The health checks are green. Now what? At the AI for Work Summit in NYC, @TheNJDevOpsGuy, AI Architect & Forward Deployed Engineer at @soloio_inc, will lead a workshop on “Instrumenting an Agent for Observability.” Michael works directly with organizations on AI and platform engineering implementation, so this session gets into the parts of agent systems that become difficult once they leave the demo stage. You’ll look at how to instrument an agent so you can actually trace what went wrong, including: - What a span should represent in an agent workflow - Token usage, tool-call success, loop depth, retrieval quality, and cost per task - Context propagation across the model provider, tools, and MCP servers - How to work backward from a real failure using traces and evals - How to construct and use Golden Eval sets For teams deploying agents, observability has to go beyond uptime and latency. You need to see what the agent did, where the process went off track, and why. 📍 New York City 📅 December 2–3 Early Bird Ends Friday – SAVE up to $550! #AIforWork #AgenticAI #AIEvaluation #Observability #MCP #ODSCAI
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
Recurring topics
The most frequent hashtags in the sampled posts. They describe what this account writes about; they are not a performance signal, and the catalog-wide breakdown on the hub shows how little hashtag count moves.
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Reading these numbers
A typical post picks up 4 interactions against 111K followers, an engagement rate of 0.003%. Measured over 48 original posts, its engagement rate beats 10% of 21,185 tracked accounts of a similar size. That is a reason to look at how the audience behaves - reply depth, saves, whether the followers are recent - rather than a conclusion about it on its own. Posts are seen about 619 times each, and 0.565% of those impressions turn into an interaction. That is about 0.56% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 2 posts a day over the last 30 days, though only 37% of days saw any activity at all. Most posts go out around 23:00 UTC, and Thursday is the busiest day of the week. Of the 48 posts sampled, 60% carry an image or video, 17% are part of a thread and 83% link out. The account's strongest tracked post pulled 8 interactions, about 2.0x its own typical post. Recurring topics include #ai, #artificialintelligence, #datascience.
- What is ODSC (Open Data Science Conference) AI's engagement rate on X?
- ODSC (Open Data Science Conference) AI (@_odsc) has an engagement rate of 0.003%, based on the median interactions across 48 original posts from the last 30 days against 110,569 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.003%, ODSC (Open Data Science Conference) AI sits below the 10th percentile of the 212,567 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @_odsc have real engagement?
- Its engagement rate beats 10% of the tracked X accounts closest to it in follower count (21,185 accounts), which puts it in the bottom 10% for its size group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
- When does @_odsc post?
- Most posts go out around 23:00 UTC, and Thursday is its busiest day, at roughly 2 posts per day across the measured window.