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Followers
3
Account age
0 yrs
💰 Suggested sponsorship rate
$40 – $65 / sponsored post
Estimated from audience size & typical reach — every deal is negotiable and held in SocialDB escrow.
🧰 Free analysis for Arize AI
📊 Activity & format
Posting cadence
7.6 / week
Toots daily — consistently active for a campaign window.
Content mix
Mostly text
Recent: 9 text · 2 image · 1 video.
Follower / following
3×
Follows 1 back. A more reciprocal / networked account.
Recent posts
View on Mastodon ↗
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Want to master the full workflow of shipping reliable AI agents? Laurie's workshop from AI Engineer World's Fair, "Evaluating and Shipping AI Agents That Work," is now a free, self-paced course on Arize University.
Earn a certificate you c…
The broader playbook on agent observability from @bookingcom:
- Trace model calls, retrieval, tools, guardrails, and fallbacks
- Monitor quality alongside latency and token usage
- Pull real failures into datasets for experimentation
- Cen…
Both problems became much easier to fix once the team could connect production signals to individual traces, configurations, and conversations.
Two AI observability lessons from @bookingcom:
- An agent latency spike came from a model running without the appropriate service tier.
- Multi-turn eval scores fell because long URLs were being added back into the conversation history, ca…
Claude Opus 5 is available in Arize AX, supported across Anthropic API, AWS Bedrock, and Vertex AI!
Opus 5 brings major improvements for long-running agents in coding and professional work.
Instrument, evaluate, and improve your agents wit…
Developers and product managers should optimize for cost per successful task, then use evals and traces to understand where each model belongs.
Get the benchmark data: https://arize.com/blog/cost-per-successful-task-ai-model-benchmark
Token price tells you what a model costs to call. It does not tell you what it costs to finish the job.
Arize and @FireworksAI_HQ benchmarked 10 models across 2,400 agent runs, including Kimi K3.
In his tests, Arize's Head of DevRel @seldo…
Gemini 3.6 Flash and Gemini 3.5 Flash-lite are now available in Arize AX on day 0!
Both models are supported on direct Gemini and Vertex AI providers, with configurable thinking levels, 65K max output tokens, and audio file input.
Trace, e…
A practical adoption path:
• Start with one bounded workflow
• Define the evidence required to merge
• Route changes by risk
• Turn corrections into eval cases
• Let failed evals trigger diagnosis
Go inside Cursor’s agent factory: https://…
Failed evals can begin the diagnosis process automatically.
The failure triggers a workflow with the relevant traces and logs already attached, allowing the system to move directly from detection to investigation.
Human review also improves future runs.
When Cursor’s Bugbot misses an issue and a developer corrects it, that correction can become a rule or regression case that tests whether the review agent catches the same failure again.
The developer environment becomes part of the evaluation harness.
When an agent can boot the product, use the interface, and record the result, reviewers can inspect the behavior before reading the diff. Tests still cover the broader state…
🐘 Community & instance
Home server
fosstodon.org
A open-source software server — its members and audience skew toward that niche, so expect a community-aligned, engaged following.
On Mastodon since
Nov 2025
An established account with real history on the platform.
💡 Facts
🗓️Joined Mastodon in 2025.
📤Posts about 7.6× per week.
🕵️ Fake follower check
Estimated50/100
Fair
Credibility score
77%
Mixed
Real audience
Medium
Fake-follower risk
Low
Data confidence
- Est. 77% real, active audience · Medium fake-follower risk.
- Limited engagement data — score uses the signals available.
Low confidence — few signals are public for this listing, so treat it as a rough screen. Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.
About
Arize is an AI engineering platform focused on evaluation and observability. It helps engineers develop, evaluate, and observe AI applications and agents.
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🔀 Audience overlap
EstimatedEstimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.
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