Amazing UX quality and speed of this ship, @hassiebpakzad
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It was a blast in India 🇮🇳 . The customers i met there ship great products at an incredible pace.
I love to present product demos over slides. It shows the progress the team made over the last months and sometimes I am even surprised by new changes made by the team during presentations.
One of the best parts of @ClickHouseDB Open House Bangalore was that the @langfuse session wasn’t just slides - it had a proper demo too.
The biggest takeaway for me:
AI failures don’t just show up in the output but they hide in the path.
Seeing the trace flow broken down step by step made that super clear:
retrieval, context, response generation, evals, monitoring : all visible instead of guesswork.
Really liked how @maxdeichmann showed the full loop:
trace → evaluate → improve → ship
And the live demo made it click even more - this is what reliable AI/agent infra actually looks like in practice. ⚡
@langfuse + @ClickHouseDB is a seriously strong combo for building and improving AI agents.
#ClickHouse #Langfuse #LLMOps #AIObservability #AI #OpenHouseBangalore
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Just boarded the plane to Bengalore to meet customers at @ClickHouseDB openhouse conference. Anyone in town who i should be meeting?
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I'm speaking at @ClickHouseDB Open House in APJ next month
sep 22 bangalore: workshop on observing and improving AI agents with langfuse
sep 24 singapore: talk on shipping reliable AI agents
say hi if you're around 👋
clickhouse.com/openhouse/ban…
clickhouse.com/openhouse/sin…
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Max Deichmann retweeted
👊I've joined the crew at @ClickHouseDB to establish the *ClickHouse Labs* research group. We'll be working side-by-side with ClickHouse devs to cook up new ways to make the system even faster. I'll also be exploring new topics related to PostgreSQL. clickhouse.com/blog/andy-pav…
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If you want to work on AI, open source, dev tools and want to sit on a rocket: join Langfuse engineering in Europe
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I wanted this so long
any table is a chart.
toggle the observations table into a time series over the exact same query. same filters, same time range. p95 latency by model, count by environment, cost by name. one click adds it to a dashboard.
langfuse.com/changelog/2026-…
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Max Deichmann retweeted
any table is a chart.
toggle the observations table into a time series over the exact same query. same filters, same time range. p95 latency by model, count by environment, cost by name. one click adds it to a dashboard.
langfuse.com/changelog/2026-…
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So excited to finally have the FE and design power on the team for such changes.
Loving the new quick filter options that Trang and @nkabardin introduced, makes it so much faster to spot anomalies 🤩
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Learning of last week: I remember some basic k8s mental models from my previous job. With AI I it is now possible to navigate the complex k8s API to launch preview deployments in ~3 days of working. Luckily we have public docs on infra and a helm chart already for OSS deployments
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Max Deichmann retweeted
Code evaluators were my first big feature at Langfuse.
Adoption is growing every day, and it has been really rewarding to see how many people already use.
And also: How many immediately tried to break the tenant isolation.
code evaluators means running untrusted user code across thousands of tenants in our multi-tenant cloud.
@wochinge wrote about the full design: (V8 isolates, pyodide), gVisor and firecracker, and kubernetes jobs.
langfuse.com/blog/2026-06-22…
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This is big! After we launched the new filter side bar, which helps users to explore their data fast, we now launch a search bar to unlock more complex filter capabilities.
Today we shipped the Filter Search Bar at @langfuse. It's my first feature since joining, so I wanted to share how we've built it. 🧵
ALT Langfuse filter/search bar filled with query
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Max Deichmann retweeted
Today we shipped the Filter Search Bar at @langfuse. It's my first feature since joining, so I wanted to share how we've built it. 🧵
ALT Langfuse filter/search bar filled with query
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The best integrations are boring.
SDKs, exporters, wrappers, and runtimes all produce slightly different telemetry. Sometimes numeric OTLP attributes arrive string-encoded. Sometimes metadata shape differs by provider.
We keep investing in these small edge cases so Langfuse observability works out of the box, not only in the happy-path demo.
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One of the coolest clickhouse fetures: the query log.
When working with postgres i was always missing a table to see how queries actually performed and how many resources they consumed.
Today, we use this to tag all queries with feature + tenant metadata to attribute infra cost.
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Its important to have a mix of signals when judging quality of traces: human annotation, LLMs, and now deterministic code. super excited to finally allow running small cloud functions on each observation ingested into Langfuse.
day 4 of langfuse launch week: code evaluators.
write a python or typescript `evaluate` function in the langfuse UI. attach it to live observations or an experiment. scores land natively next to your existing ones.
@wochinge demos below; langfuse.com/launch
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This is one of the most powerful recent launches of us. As Langfuse is increasingly used by Agents, FTS is the best way to help agents to find what it needs in terrabytes of user data. Thanks for the ship @sum3rman !
day 3 of langfuse launch week: full-text search.
multi-GB scans drop from many seconds to sub-second on @ClickHouseDB's new text indexes. great work from @sum3rman.
available via UI and API.
more: langfuse.com/launch
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This also shows me how valuable the @ClickHouseDB acquisition was!
Our team meet at our Tokyo offsite with the engineer who built CH FTS to discuss scalability, access patterns, and potential performance bottlenecks.
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