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Charly Wargnier ♨️
Charly Wargnier ♨️182K
Charly Wargnier ♨️Verified

DevRel • Living and breathing LLMs, agents, automation and data science. prev. @Snowflake | @Streamlit | @Samsung • my ♥ is open-source • 📩 DM for Collabs

@datachazAI AgentsLarge Language ModelsOpen Source AI
182KFollowers
16.7KMedian impr. / post
1.1%Engagement
13+8
Paid partnerships
In their audience
6+ affiliated with @microsoft follow Charly Wargnier ♨️
@_MoustafaM@jnnfir@Richie4love@LiatBenZur@RakeshSingh_@01Phenomenon6+ from @ycombinator4+ affiliated with @google2+ from @umich2+ affiliated with @spacexai2+ affiliated with @googledeepmind
1,500+ verified followers analyzed
@_MoustafaM@jnnfir@Richie4love@LiatBenZur@RakeshSingh_@01Phenomenon+149490+ AI founders83+ AI engineers68+ founders56+ media creators42+ engineers35+ executives
25–34 core age group
39.7%
22.1% of audience in United States
🇺🇸
Sep 25 – Oct 1105 posts analyzed · replies not counted · updates weekly
ImpressionsEngagements
899K450K0
8.3K4.2K0
Sep 25Sep 26Sep 27Sep 28Sep 29Sep 30Oct 1
Past collabs
Charly Wargnier ♨️Verified@datachaz · Sep 25OPUS 5.5 IS REALLY A DIFFERENT BEAST 🤯 In @aimlapi’s latest test, 4 of the hottest models get locked in a tiny box with a Rubik’s Cube. > Jev and Laya fire off 20 random moves in seconds > GPT-6 Sol makes one bad choice and gives up > Opus 5.5 waits a minute… then solves it in 👏 three 👏 moves ↓aimlapiPaid partnership26.3K views
Charly Wargnier ♨️Verified@datachaz · Sep 24STT MOSTLY BEAT BACKGROUND NOISE But it still loses when another person starts talking nearby. @krispHQ just open-sourced its 'Krisp Voice Isolation' Benchmark on @HuggingFace to test exactly that: how much can voice isolation improve speech-to-text? > 265 real recordings > 11 STT setups > Word error rate: 23.3% → 6.2% with isolation That’s roughly a 73% reduction. The gains were even bigger in shared offices and call-center environments. Phone calls barely moved, which makes sense. There’s usually much less competing speech to remove. → real recordings, not synthetic mixes → complete dataset on Hugging Face → fully reproducible Krisp Voice Isolation already powers 200+ voice platforms and has processed 10B+ minutes of voice-agent audio. Now the benchmark data is open 🤗 (link in 🧵 ↓)KrispPaid partnership8.8K views
Charly Wargnier ♨️Verified@datachaz · Sep 2394% OF ENGINEERING ORGS USE AI. ONLY 6% HAVE THE SYSTEMS IN PLACE TO SCALE IT So how do we close that gap? and what are the systems that the 6% have in place? This is exactly what companies like @Atlassian, @Vercel, @lovable, and more address during State of the AI SDLC, a digital summit for engineering & product leaders navigating the realities of building with AI. If you watch any of the on demand sessions, you can’t miss 'Context Over Code': @mcannonbrookes (CEO + Co-Founder, Atlassian) & @rauchg (CEO, Vercel) sit down and unpack how to navigate the shift from adopting tools to building actual systems. Then, I recommend watching @matthewcanham & Ming Wu’s session on how they scaled the AI SDLC at Atlassian across an organization of over 6k+ engineers. My key takeaways: To scale across an entire engineering org you need governed loops that connect agent activity to the work already happening. These sessions provide a literal blueprint for moving from pilot to production. → context dictates output quality → native workflows require systems of record → scaling requires governed loops The summit sessions are available on-demand right now. Session link in the 🧵 ↓AtlassianPaid partnership9K views
Charly Wargnier ♨️Verified@datachaz · Sep 23🚨 ORACLE AND @DEEPLEARNINGAI JUST DROPPED A MASTERCLASS ON ADAPTIVE AGENTS. SO MUCH value packed into one hour, and it's completely FREE 🤯 The harsh reality right now is that AI agents forget everything when a session ends, forcing you to pay for the exact same mistakes every. single. day. Nacho Martínez and Casius Lee teamed up with @AndrewYNg and @DeepLearningAI and to completely dismantle this problem. The pacing of this curriculum is what makes it great. In just over an hour, it teaches you how to architect 3 heavy adaptation layers from scratch: ▪ Part 1 - Behavior: → you will convert agent traces into reusable skills, setting up a human approval gate to ensure only verified logic moves forward. ▪ Part 2 - Knowledge: → you will map your codebase by building a knowledge graph from imports and historical Git co-edits, proving exactly why graph retrieval beats basic keyword search. ▪ Part 3 - Model: → you will learn to identify the exact thresholds where fine-tuning and weight-space methods become necessary for your stack. You will leave with a complete understanding of how to make agents measurably better with every run. link to free course in 🧵 ↓DeepLearningAIPaid partnership29.9K views
Charly Wargnier ♨️Verified@datachaz · Sep 23A GOOD PRODUCT CAN WIN A CUSTOMER. A 40-MINUTE HOLD WHEN IT BREAKS CAN LOSE THEM. @Cresta’s new Beast Mode Button puts AI on the routine requests, then hands over the convo with context when a human is needed. That’s pretty cool.CrestaPaid partnership5.1K views
Charly Wargnier ♨️Verified@datachaz · Sep 23ONE IDE. EVERY AGENT. Mirasim is building a single workbench for agentic coding. Claude Code, Codex, different models, tools, skills and plugins.. all in one place. > run multiple projects in parallel > give each session its own agent + model > switch between supported agents without losing the conversation this is where AI coding environments are heading 👀MirasimPaid partnership5.3K views
Charly Wargnier ♨️Verified@datachaz · Sep 22SPREADSHEETS LOOK STRUCTURED. THE DATA OFTEN ISN’T. 74% of finance professionals reportedly spend 5+ hours a day in Excel. And @GetUnstract is tackling one of the messiest parts: getting that data reliably into ERPs, CRMs and downstream workflows. Because real spreadsheets come with: → merged + hierarchical headers → data scattered across tabs → cross-sheet dependencies → layouts that constantly change → scans and handwritten inputs → fields that still need human review .. and more Humans understand that structure almost instantly. Traditional extraction pipelines need rigid assumptions. Agentic extraction changes the model: reason about the structure, identify what matters and adapt as the spreadsheet changes. Unstract is showing this live on real-world messy spreadsheets. 100% open source. Repo + webinar links in the 🧵↓UnstractPaid partnership4.5K views
Charly Wargnier ♨️Verified@datachaz · Sep 22A NEW CLOUD ERA FOR AI AGENTS IS HERE Claude Code. Codex. Hermes. OpenCode. LangGraph. Even your own container 🤯 You bring the agent. @DigitalOcean Managed Agents handles the infrastructure underneath. now in Public Preview 👀DigitalOceanPaid partnership5.4K views
Charly Wargnier ♨️Verified@datachaz · Sep 1860 ad campaigns. 29 markets. Optimised twice a day. All run by the same team that used to manage just 5. That is what an agent in production actually looks like. Writing about why agents fall over once they leave the demo is most of what I do here. The failure mode is almost never reasoning. It is that every tool in the chain behaves like an isolated employee with no memory of what the last one learned. You get collective amnesia plus a human doing the handoffs by hand. Rigid workflows were my first fix and they broke immediately, because production is where the shape of the work changes underneath you. @viktor_com takes the other road. He is an AI employee inside Slack that reaches around 3,200 tools through one connection rather than a graph of brittle integrations, holds context across the whole job, and returns a proposal for approval instead of firing blind. That gate is what makes it survivable in production. The part I did not prompt: he surfaced that two connected sources were reporting the same metric on different windows. Antonin Stetina, CEO of KULINA Group, put it plainly: "Mindblowing all-in-one AI which does everything in a single solution." Twelve times the campaign volume on $2.5M in spend, no headcount added. Architecture beats model choice. Again. Try him free at @viktor_com. $100 in credits, no card. Link in 🧵 ↓ViktorPaid partnership89.5K views
Charly Wargnier ♨️Verified@datachaz · Sep 18FAKING YOUR DETAILS MANUALLY RUINS THE AI OUTPUT Meet AgentCloak > AgentCloak runs in your browser and swaps your real info for fake info > it swaps your real info back into the answer > AI gets dummy data many companies trust it. now it is free 👊 → http://agentcloak.aiAgentCloakPaid partnership35.3K views
Charly Wargnier ♨️Verified@datachaz · Sep 1784% OF THE SDLC HAPPENS OUTSIDE THE CODE AI agents are making execution faster. but intent, context, coordination, review + decision making still sit around it. That’s the bigger question CEOs and leaders from @Atlassian, @Vercel, @Lovable, @Dropbox, and @Honeycomb are digging into at its State of AI SDLC summit on Sept 22 in conversations with industry leaders. Across 6 sessions you’ll: > explore how is is reshaping the software lifecycle > learn how leaders are measuring AI’s real impact > discover how context drives better agent outcomes one session I’m particularly excited about: "6K Engineers. 1M Agents. Scaling the AI SDLC at Atlassian" @matthewcanham and @mingwu1 from Atlassian are digging into what it actually takes to scale agents across thousands of engineers. I want to hear what changes, what breaks, what needs to be redesigned when you start operating AI at that level 👀 Spaces are limited, details in 🧵↓AtlassianPaid partnership15.8K views
Charly Wargnier ♨️Verified@datachaz · Sep 15https://poolday.ai: > learns your brand once > takes a video brief > finds + cuts the footage > adds assets, captions + animations > QA’s the finished video video freelancers reading this:PooldayPaid partnership64.9K views
Charly Wargnier ♨️Verified@datachaz · Sep 15POV: telling your wife an AI called Bob took your jobBuildBetterPaid partnership48.9K views
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