Top Tweets for #AutoLab
🔬 #Autolab vs #DropSens: performance, flexibility and future growth explained. More articles: https://t.co/UopLxZLWFZ ⚡🧪
#Potentiostats #Impedance #Electrochemistry
🧪Great day at #CHEMUKEXPO 2026❗️
Fantastic chats on the @Metrohm_UK_IRE #Autolab #Vionic & #PGSTAT302N.
See you at Booth G76 tomorrow ⚡
📍At #CHEMUK tomorrow #NECBirmingham🔬Still a few meeting slots free💬DM me to connect ⚡
@Metrohm_UK_IRE @ExpoChemuk42481
#Metrohm #Electrochemistry #Autolab #DropSens
🇮🇪 Arriving in #Ireland soon! Visiting Cork, Galway, Limerick & Dublin. DM to connect 🔬⚡🌈
#Metrohm #Autolab #Dropsens
@Metrohm_UK_IRE
#Potentiostats #Galvanostats #SpectroElectrochemistry
🎓 Great day with @Metrohm_UK_IRE at the @UoNScience meeting brilliant researchers 🔬
⚡ Exciting discussions on #electrochemistry innovation in #batteries, #fuelcells, #diagnostics, #materialsscience & more 🧪
Using #potentiostats or #galvanostats? DM me 💬
#Metrohm #Autolab
Since launching #AutoLab, we’ve gotten a lot of inbound from researchers, builders, and friends.
What’s clear is this: the field wants a better standard for evaluating research-capable agents.
Our goal is simple: build a fair, open, transparent benchmark for agents that can operate in real scientific and engineering loops.
This should not be defined behind closed doors.
[Accidentally deleted this earlier, reposting] 😭
#AutoLab #autoresearch
We've been asking ourselves a question: if AI agents can now run hundreds of experiments overnight, how do we know whether they're actually contributing to research — or just generating noise?
That's why we built AutoLab (https://t.co/aRbV2YeaPf).
Not another pass/fail benchmark, but an open-source environment where agents face the same loop every researcher knows intimately — propose, test, fail, diagnose, revise, repeat.
23 tasks with no answer keys, just open search spaces and real constraints.
We ran 161 evaluations across 7 frontier models, 633M tokens. Every decision, every pivot, every dead end — all openly available in our Live Lab for anyone to replay and learn from.
What we found wasn't about which model is "smartest." It's about a capability we call closed-loop resilience: when incremental refinement stops working, can the agent recognize it and restructure? On one task, two frontier models hit the same wall. One kept pushing within the existing frame. The other stepped back and redesigned the approach entirely. That moment — knowing when to abandon a frame, not just optimize within it — is what separates real research from sophisticated pattern matching.
We believe this matters beyond benchmarking. If agents are genuinely entering the research loop, we want that transition to be measured transparently, built in the open, and shaped by the community — not locked inside any single lab. The scientist doesn't disappear. The loop gets a new participant. And we want to make sure that participant is understood.
This is a joint effort across @Stanford, @MIT, @UW, @UCSanDiego, @ucsantabarbara, @NotreDame, NUS, @Google, @NVIDIA, @IBMResearch, and @bakelab_hq. But 23 tasks is just the start. If you have an optimization problem you've spent weeks grinding on empirically — with a clear metric and no known optimal solution — it probably belongs here. Contribute a full task, a rough skeleton, or just the idea.
The best benchmarks aren't built by one team. They're built by the people who actually do the work!
Github: https://t.co/2sLNlASVcb
![my_cat_can_code's tweet photo. [Accidentally deleted this earlier, reposting] 😭
#AutoLab #autoresearch
We've been asking ourselves a question: if AI agents can now run hundreds of experiments overnight, how do we know whether they're actually contributing to research — or just generating noise?
That's why we built AutoLab (https://t.co/aRbV2YeaPf).
Not another pass/fail benchmark, but an open-source environment where agents face the same loop every researcher knows intimately — propose, test, fail, diagnose, revise, repeat.
23 tasks with no answer keys, just open search spaces and real constraints.
We ran 161 evaluations across 7 frontier models, 633M tokens. Every decision, every pivot, every dead end — all openly available in our Live Lab for anyone to replay and learn from.
What we found wasn't about which model is "smartest." It's about a capability we call closed-loop resilience: when incremental refinement stops working, can the agent recognize it and restructure? On one task, two frontier models hit the same wall. One kept pushing within the existing frame. The other stepped back and redesigned the approach entirely. That moment — knowing when to abandon a frame, not just optimize within it — is what separates real research from sophisticated pattern matching.
We believe this matters beyond benchmarking. If agents are genuinely entering the research loop, we want that transition to be measured transparently, built in the open, and shaped by the community — not locked inside any single lab. The scientist doesn't disappear. The loop gets a new participant. And we want to make sure that participant is understood.
This is a joint effort across @Stanford, @MIT, @UW, @UCSanDiego, @ucsantabarbara, @NotreDame, NUS, @Google, @NVIDIA, @IBMResearch, and @bakelab_hq. But 23 tasks is just the start. If you have an optimization problem you've spent weeks grinding on empirically — with a clear metric and no known optimal solution — it probably belongs here. Contribute a full task, a rough skeleton, or just the idea.
The best benchmarks aren't built by one team. They're built by the people who actually do the work!
Github: https://t.co/2sLNlASVcb](https://pbs.twimg.com/media/HFCRHmmaAAAwiuO.jpg)
![my_cat_can_code's tweet photo. [Accidentally deleted this earlier, reposting] 😭
#AutoLab #autoresearch
We've been asking ourselves a question: if AI agents can now run hundreds of experiments overnight, how do we know whether they're actually contributing to research — or just generating noise?
That's why we built AutoLab (https://t.co/aRbV2YeaPf).
Not another pass/fail benchmark, but an open-source environment where agents face the same loop every researcher knows intimately — propose, test, fail, diagnose, revise, repeat.
23 tasks with no answer keys, just open search spaces and real constraints.
We ran 161 evaluations across 7 frontier models, 633M tokens. Every decision, every pivot, every dead end — all openly available in our Live Lab for anyone to replay and learn from.
What we found wasn't about which model is "smartest." It's about a capability we call closed-loop resilience: when incremental refinement stops working, can the agent recognize it and restructure? On one task, two frontier models hit the same wall. One kept pushing within the existing frame. The other stepped back and redesigned the approach entirely. That moment — knowing when to abandon a frame, not just optimize within it — is what separates real research from sophisticated pattern matching.
We believe this matters beyond benchmarking. If agents are genuinely entering the research loop, we want that transition to be measured transparently, built in the open, and shaped by the community — not locked inside any single lab. The scientist doesn't disappear. The loop gets a new participant. And we want to make sure that participant is understood.
This is a joint effort across @Stanford, @MIT, @UW, @UCSanDiego, @ucsantabarbara, @NotreDame, NUS, @Google, @NVIDIA, @IBMResearch, and @bakelab_hq. But 23 tasks is just the start. If you have an optimization problem you've spent weeks grinding on empirically — with a clear metric and no known optimal solution — it probably belongs here. Contribute a full task, a rough skeleton, or just the idea.
The best benchmarks aren't built by one team. They're built by the people who actually do the work!
Github: https://t.co/2sLNlASVcb](https://pbs.twimg.com/media/HFCQ_ltbkAAWset.jpg)
![my_cat_can_code's tweet photo. [Accidentally deleted this earlier, reposting] 😭
#AutoLab #autoresearch
We've been asking ourselves a question: if AI agents can now run hundreds of experiments overnight, how do we know whether they're actually contributing to research — or just generating noise?
That's why we built AutoLab (https://t.co/aRbV2YeaPf).
Not another pass/fail benchmark, but an open-source environment where agents face the same loop every researcher knows intimately — propose, test, fail, diagnose, revise, repeat.
23 tasks with no answer keys, just open search spaces and real constraints.
We ran 161 evaluations across 7 frontier models, 633M tokens. Every decision, every pivot, every dead end — all openly available in our Live Lab for anyone to replay and learn from.
What we found wasn't about which model is "smartest." It's about a capability we call closed-loop resilience: when incremental refinement stops working, can the agent recognize it and restructure? On one task, two frontier models hit the same wall. One kept pushing within the existing frame. The other stepped back and redesigned the approach entirely. That moment — knowing when to abandon a frame, not just optimize within it — is what separates real research from sophisticated pattern matching.
We believe this matters beyond benchmarking. If agents are genuinely entering the research loop, we want that transition to be measured transparently, built in the open, and shaped by the community — not locked inside any single lab. The scientist doesn't disappear. The loop gets a new participant. And we want to make sure that participant is understood.
This is a joint effort across @Stanford, @MIT, @UW, @UCSanDiego, @ucsantabarbara, @NotreDame, NUS, @Google, @NVIDIA, @IBMResearch, and @bakelab_hq. But 23 tasks is just the start. If you have an optimization problem you've spent weeks grinding on empirically — with a clear metric and no known optimal solution — it probably belongs here. Contribute a full task, a rough skeleton, or just the idea.
The best benchmarks aren't built by one team. They're built by the people who actually do the work!
Github: https://t.co/2sLNlASVcb](https://pbs.twimg.com/media/HFCQ6ueasAAGnHI.jpg)
![my_cat_can_code's tweet photo. [Accidentally deleted this earlier, reposting] 😭
#AutoLab #autoresearch
We've been asking ourselves a question: if AI agents can now run hundreds of experiments overnight, how do we know whether they're actually contributing to research — or just generating noise?
That's why we built AutoLab (https://t.co/aRbV2YeaPf).
Not another pass/fail benchmark, but an open-source environment where agents face the same loop every researcher knows intimately — propose, test, fail, diagnose, revise, repeat.
23 tasks with no answer keys, just open search spaces and real constraints.
We ran 161 evaluations across 7 frontier models, 633M tokens. Every decision, every pivot, every dead end — all openly available in our Live Lab for anyone to replay and learn from.
What we found wasn't about which model is "smartest." It's about a capability we call closed-loop resilience: when incremental refinement stops working, can the agent recognize it and restructure? On one task, two frontier models hit the same wall. One kept pushing within the existing frame. The other stepped back and redesigned the approach entirely. That moment — knowing when to abandon a frame, not just optimize within it — is what separates real research from sophisticated pattern matching.
We believe this matters beyond benchmarking. If agents are genuinely entering the research loop, we want that transition to be measured transparently, built in the open, and shaped by the community — not locked inside any single lab. The scientist doesn't disappear. The loop gets a new participant. And we want to make sure that participant is understood.
This is a joint effort across @Stanford, @MIT, @UW, @UCSanDiego, @ucsantabarbara, @NotreDame, NUS, @Google, @NVIDIA, @IBMResearch, and @bakelab_hq. But 23 tasks is just the start. If you have an optimization problem you've spent weeks grinding on empirically — with a clear metric and no known optimal solution — it probably belongs here. Contribute a full task, a rough skeleton, or just the idea.
The best benchmarks aren't built by one team. They're built by the people who actually do the work!
Github: https://t.co/2sLNlASVcb](https://pbs.twimg.com/media/HFCQyGgbMAAZw3X.jpg)
[Accidentally deleted this earlier, reposting] 😭
#AutoLab #autoresearch
We've been asking ourselves a question: if AI agents can now run hundreds of experiments overnight, how do we know whether they're actually contributing to research — or just generating noise?
That's why we built AutoLab (https://t.co/aRbV2YeaPf).
Not another pass/fail benchmark, but an open-source environment where agents face the same loop every researcher knows intimately — propose, test, fail, diagnose, revise, repeat.
23 tasks with no answer keys, just open search spaces and real constraints.
We ran 161 evaluations across 7 frontier models, 633M tokens. Every decision, every pivot, every dead end — all openly available in our Live Lab for anyone to replay and learn from.
What we found wasn't about which model is "smartest." It's about a capability we call closed-loop resilience: when incremental refinement stops working, can the agent recognize it and restructure? On one task, two frontier models hit the same wall. One kept pushing within the existing frame. The other stepped back and redesigned the approach entirely. That moment — knowing when to abandon a frame, not just optimize within it — is what separates real research from sophisticated pattern matching.
We believe this matters beyond benchmarking. If agents are genuinely entering the research loop, we want that transition to be measured transparently, built in the open, and shaped by the community — not locked inside any single lab. The scientist doesn't disappear. The loop gets a new participant. And we want to make sure that participant is understood.
This is a joint effort across @Stanford, @MIT, @UW, @UCSanDiego, @ucsantabarbara, @NotreDame, NUS, @Google, @NVIDIA, @IBMResearch, and @bakelab_hq. But 23 tasks is just the start. If you have an optimization problem you've spent weeks grinding on empirically — with a clear metric and no known optimal solution — it probably belongs here. Contribute a full task, a rough skeleton, or just the idea.
The best benchmarks aren't built by one team. They're built by the people who actually do the work!
Github: https://t.co/2sLNlASVcb
Spring is here 🌸
Warmer weather means more driving. Stop in and make sure everything is running right this season!
#spring2026 #autorepair #autolab
Don’t test your luck 🍀
That “I’ll wait a little longer” mindset is dangerous.
Get your car checked before it checks out.
#autolab #autorepair #StPatricksDay2026
Este sábado temos o PREGOBÁN, obradoiro de dobrado victoriano para facer fanzines despregables 🙀✨
Só tedes que traer material de debuxo, nós poñemos o papel!!
🗓️ Sábado 14 de marzo
🕖 Ás 12:00 h
📍 En Lume (Rúa Fernando Macías 3)
#autoban #autolab #clubdebuxo #pregoban
Este sábado repetimos o Teléfono Gráfico📞✏️ na Revolteira, cun picnic indoor improvisado ✨
🗓️ Sábado 28 de febreiro
🕖 Ás 12:00 h
📍 Revolteira (Rúa Falperra 13)
NON HAI QUE APUNTARSE, só vide e traede o material de debuxo que queirades 🎨
#autoban #autolab #clubdebuxo
Roses are red 🌹
Violets are blue 💙
If your car needs some love, 💕
We’ve got you. 🚗✨
#Autolab #ValentinesDay #AutoRepair
Temos todo preparado para o Club de Debuxo deste sábado no Pub El Siglo: o STOPBÁN!
🗓️ Sábado 24 de xaneiro
🕖 Ás 18:30 h
📍 No Siglo (Rúa Argudín Bolívar 1)
NON HAI QUE APUNTARSE, só ven e trae o material de debuxo que queiras 🎨
#autoban #autolab #clubdebuxo #stop #stopban
Ready to shift gears and drive your own success? 🚗💼 Auto-Lab franchises are your chance to own a proven auto care business with full support and a trusted brand. Start your journey today! 👉https://t.co/NaUzDdhflg
#FranchiseOpportunity #AutoLab
Your tires called… they’re tired of slipping on ice. 🧊 Give them some love with a rotation or check today! #AutoLab #WinterDriving #CarCare
2026 NEW YEAR’S RESOLUTION: Stop ignoring the check engine light.🚨It means something is wrong.
Book your FREE 15-minute inspection today!
#AutoLab #2026Goals
¿Qué valoran nuestros clientes de Metrohm? Sin duda, la gran confiabilidad en los resultados y la durabilidad de nuestros equipos. Preguntamos en el 𝗫𝗟 𝗖𝗼𝗻𝗴𝗿𝗲𝘀𝗼 SMEQ y la Dra. Margarita nos comenta la gran experiencia que es el usar su #Potenciostato #autolab PGSTAT302N
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