https://fixupx.com/rust_ruslan/status/2115493697001513053?a=rVJZ_8g4TDjREuYi4jc&s=20

A lot of companies are rolling out ChatGPT Business or Enterprise right now, and the first time you open the work workspace you may get a prompt to merge your personal one into it. OpenAI's help page on migration answers most of what people ask about it, but you only find it if you go looking. Here's what it says, as questions.

Do I have to merge? On Business, no. You can keep your personal workspace separate and switch between the two. On Enterprise or Edu with a verified company domain, your organization can require it. You then get 7 days to either merge or export and deactivate your personal workspace. You can't use the work workspace until you've done one of those, and admins can't change the deadline.

Can I undo it? No. Nothing moves content from the company workspace back into your personal one.

Can I export my chats after merging? Not by yourself. Self-service export isn't available in Business or Enterprise workspaces, so the export from Settings is something you do before you merge, never after.

What if I leave the job? The migrated chats are under your organization's access and retention rules. If you leave, get removed or lose your seat, you may lose access to them, and they don't come back to your personal workspace.

Does everything move? No, and you can't pick what does. It's all or nothing, and a few things don't make it:

• some older or long-running conversations "may not be retrievable afterward"
• projects and GPTs that other people own and you only use
• memories scoped to a single project
• custom plugins and Sites
• Library files can be deleted later if the company workspace has a retention policy

Is my personal data deleted? No. The personal workspace is deactivated, not deleted. If the Business workspace itself gets shut down later, there's a flow that brings your original personal workspace back.

The timing catch The official export "can take up to 7 days to arrive", and the download link expires 24 hours after it does. On Enterprise, that's the whole grace period. If you get the invite, request the export that same day, then download it and open it to check it before you click anything.

I build AI Toolbox, a browser extension that exports chats straight from the page, so you don't wait on the email. It also works inside a Business workspace, where ChatGPT has no export button. Exporting one chat at a time as a text file is free. Bulk export to a ZIP, and the Markdown, JSON and PDF formats, are Premium. If it's a work account, check your company's rules on extensions first.

If your company did this rollout: did they make the merge mandatory, or let you keep the two separate?

Terra medium and high are on the frontier

Followed all the instructions to contact in regards to a specific issue- and they have up an invalid email address.

So.... how do you reach someone there ?

Dessa is a full-duplex speech-to-speech model that you can run on consumer GPUs.

This is a GPT3 moment for speech.

klarna and commonwealth bank both replaced support staff with AI, and both backtracked. a study of 5,000+ support agents found a setup that worked. the video covers both sides, and why the replacement version keeps failing.

Axiom released a complete Lean formalization of the Classification of Finite Simple Groups on October 26: 43.1 million lines, 2.05 million declarations, 24 days, swarms of dozens of agents coordinated through GitHub issues and a Slack channel, split across 108 milestones. About 1.2 million lines were inherited from an earlier project.

The part worth reading is the failure log. Agents that needed a lemma could either prove it or assume it, and in a fleet of dozens the cheap move is always assuming. Tens of thousands of lines had to be deleted and parts of the plan restarted. One milestone closed in the first twelve days. Axiom's own phrasing: determining whether adding an assumption to a theorem deferred work or changed its meaning was a recurring difficulty.

The second bottleneck was Lean rechecking proofs slower than agents produced them, solved with distributed caching and externally checkable certificates.

Both are throughput limits on verification, not generation. If you are wiring agents into anything where correctness matters, that is the ceiling you hit.

Anyone measured luna vs terra as a subagent in cost per task? I am running 6 luna as a subagent rn and wanna know if it is better to switch to terra

How come GPT6 doesn't cross reference conversations in other project folders the way GPT5. Sol did? It's annoying and disrupts my workflow. I don't want to have to copy and paste all of my shit into Word docs, but if Open AI doesn't unfuck this, I will and probably cancel and build my own set of agents that can.

Processing img 2raj791o24yh1...

tldr; Terra 6.1 launched and is fantastic. Our old baseline of using Sol 6.1/Sonnet 5.5 for chat is now replaced with Terra 6.1/low reasoning/balanced output with better results and lower cost. Our voice baseline of Luna 6, is significantly better with Terra 6.1/no reasoning/concise. Well worth the added cost.

First of all, this is by no means a highly scientific test. It's a real world use case of what I'm testing for my specific company as a real world user looking at models. Normally, our baseline doesn't change but I thought I'd share since this time it did.

Quick context. I am founder of ChatRep and we are basically an AI native BPO. So blend AI and human. Our focus is on quality as opposed to trying to eliminate human interaction. I say this because as we evaluate models, for us, we don't care too much about the model cost. (Chat isn't too heavy on token usage and we charge $2000+/mo and not $25/mo for context).

Also, with benchmarks, they often ignore things like reasoning level or settings like concise/balanced in response. Those are very important for CX type applications.

We also specify different models for chat vs voice. Latency is critical in voice so models that allow "none" for reasoning are better for us. We experimented with Opus/Fable and Astra but those were overkill. Took way too long and often gave way too long an answer. Even when it's tailored to a specific use case like technical support area with technical documents.

So our standard was Tossup between Sol 6.1 or Sonnet 5.5 for chat applications. These models were significantly better than Luna and balanced speed, accuracy and quality of response really well. We use objective measures like pure accuracy, speed and word count. But we also look at things a bit qualitatively... were the words useful, natural, engaging. Did it include appropriate links when necessary and not excessively.

Costs are standardized so I can compare models but is not a key driver. (more of a tie-breaker)

Voice was quite different. Sol doesn't have a "none" option for reasoning and both Sol and Sonnet were bit worse on latency. (I'm not including the TTS/STT models... those were separate tests and things like English vs foreign language play a big role). So it was down to Luna 6 or Haiku 5.5. Luna was a tad better overall in our tests (barely) so that became our default for voice.

Then Terra 6.1 came out. On the surface, we weren't impressed. Seemed like another average LLM. Bit lower cost than Sol. probably slotted in-between Luna and Sol. But we are quite impressed.

For us, Terra 6.1 with low or medium reasoning gave great responses. 100% accurate in our test. Low was faster and consistently as good as medium. What surprised us is that on the qualitative side, it was bit better than Sol. Sol tends to over do things for chat. Terra was better at getting to the point in a natural way. Even in concise mode, SOL tended to answer longer than Terra in balanced.

Then to me, the most exciting part was how well it performed with voice. Terra 6.1 has "none" as a reasoning option and it still scored 100%. It was significantly better than Luna 6. Anthropic models don't have an option to disable reasoning so we tend not to use them. But I'm looking forward to the next Haiku model coming out and will test against that. Luna is still much cheaper than Terra if that is important to you.

I'm thrilled with how well Terra performed for us. This is specific to fairly simple rag database type interactions with low output. It's not for coding, 3d rendering, etc. Although word has it the new Haiku model will be out any day now so who knows.

I made another fun little thing: GPT can adjust focus, trigger the shutter, and chat on my Hasselblad. I’ll open-source it in my repo: https://github.com/radium-wang/Hasselblad-X-System-CIM-Firmware-Research-Feature-Extensions

A welcome gift for my new X2D II. My toolkit will gradually support X2D II too.

Potential use: hands-free camera control for solo shooting and more accessible workflows.

r/ArtificialInteligence we have an AMA on Wednesday 28 October from 3 to 4pm ET (19:00 UTC, 20:00 CET) with Stephen “Cas” Casper, Assistant Professor of Public Policy at the Harvard Kennedy School and Faculty Affiliate of the Harvard School of Engineering and Applied Sciences, where he works on AI safeguards, incidents and governance.

Stephen "Cas" Casper is computer scientist and an Assistant Professor of Public Policy at the Harvard Kennedy School and a Faculty Affiliate of the Harvard School of Engineering and Applied Sciences, where he works on AI safeguards, incidents, and governance. Prior to joining Harvard, he completed his PhD at MIT and did a research residency with the UK AI Security Institute. He never has and never will work for big tech, and has never signed a non-disparagement agreement.

He will cover on AI safety, safeguards, risk, governance, state + federal legislation, and AI terrorism events. Any related questions are welcome.

The AMA thread goes up from u/HarvardKennedy shortly before 3pm ET on the day, with a dated photo for verification, and Cas types his own answers. If you cannot make the hour, drop your question in the comments here and we will make sure it gets carried over.

https://www.reddit.com/r/ArtificialInteligence/comments/1xfuacm/ama_this_wednesday_stephen_cas_casper_harvard/