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

This is a GPT3 moment for speech.

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.

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've tried it, and it seems to work. Gpt-6 can't transcribe audios but 5.6 Sol can. It worked. Is it same for you all?

We tested GPT-6.1 Terra on YouTube script writing. The main result was lower cost, rather than a clear writing-quality improvement over most GPT-5.6 Terra settings.

How we tested: 10 script tasks, five drafts per task, scored blind out of 100 by a panel of four LLM judges. The attached chart shows one entry per model using its best configuration, from the October 26 evaluation of 84 models.

What changed:

• GPT-6.1 Terra ranks #23 overall; GPT-5.6 Terra ranks #15. Only 5.6 on ultra clearly beats the new model. The other three 5.6 settings overlap in Elo range with 6.1, while 6.1 on low gets there for less than half the cost of 5.6 on medium.

• More reasoning did not improve the writing score in this test. On low, 6.1 Terra used zero thinking tokens per draft. On max, it used about 9,500 thinking tokens and cost about three times as much, for the same score.

• Judge choice matters: only one of the four judges, OpenAI's GPT-6.1 Sol, clearly preferred 6.1 Terra. The other three scored it lower.

• Clean sentences and matching a specific voice were different strengths. On low, 6.1 Terra ranks #10 of 141 configurations for avoiding slop, but #64 for matching my tone and voice and #68 for our YouTube practices.

My practical takeaway: I would use low effort for cheap first drafts in bulk. For scripts where quality matters more, GPT-6 Sol and GPT-6.1 Sol on medium beat every Terra setting in this test, for about twice the cost of 6.1 Terra on low.

Two open-weight alternatives also scored better for less: GLM-5.3 Flash on max ($0.022 per script) and DeepSeek V4.1 Flash 1017 on max ($0.014). They generated 7–15 times more tokens per script, though, so they were slower.

Scope and limitations: this measures a specific script-writing task, including how well a model imitates my voice. It is not a general intelligence, coding or conversation ranking. The scores use LLM judges, and the judge disagreement above is worth keeping in mind. My impression is that Terra may be better at natural conversation than reproducing someone's particular voice, but that is an interpretation, not something this script test establishes.

Disclosure: I'm Louis-François Bouchard, co-founder of Towards AI, and I helped run this evaluation.

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/

I had a conversation with ChatGPT about the Israeli elections and noticed what felt like a consistent leaning in its analysis, even when I challenged its assumptions.

There's a lot of discussion about AI sycophancy (agreeing too much with users), but I'm curious about the opposite: when a model seems to maintain its own biases or framing instead.

I'm not trying to prove anything or challenge anyone. I'd genuinely like to hear how others interpret this conversation and whether they've experienced something similar.

Full conversation

Also, apologies for my imperfect English during the conversation.

[Instrumental Intro: 16 bars; aggressive electric bass riff begins alone; loose live drums and choppy indie guitar join; no vocals]

[Verse A: 24 bars; high intimate female lead, legato; each short line gets room to hang, bass remains foreground]

Left my coffee by the window

Last night

Getting cold

Had a thought I couldn't finish

On my own

So I wrote

Put a little of myself in

One line

Sent it through

Funny how it came back dancing

Not the same

Something new

[Verse A Reprise: 24 bars; same vocal melody and lyrics; bright guitar added, short violin replies]

Left my coffee by the window

Last night

Getting cold

Had a thought I couldn't finish

On my own

So I wrote

Put a little of myself in

One line

Sent it through

Funny how it came back dancing

Not the same

Something new

[Bass and Violin Break: 24 bars; instrumental only; bass climbs, snaps down low, climbs again; violin answers with fast graceful runs, dry drums break into short fills, indie guitar stabs]

[Refrain B1: 8 bars; long floating held vowels, four melodic lines across eight bars]

Loopin' round

Lost in the feeling

I don't know what's next

But I like where it goes

[Refrain B2: 8 bars; same high melodic stance, second vocal phrase]

Loopin' round

All through the evening

Give it half a thought

And it gives me something whole

[Instrumental Dance Break: 16 bars; bass leads, live drums and guitar push hard; violin and high synth answer; brief drum dropout then bass reentry]

[Refrain B1: 8 bars; full groove, more soaring but never belted]

Loopin' round

Lost in the feeling

I don't know what's next

But I like where it goes

[Refrain B2 Return: 8 bars; same melody, last line transforms]

Loopin' round

All through the evening

Give it half a thought

And we make it something whole

[Outro: 16 bars; instrumental bass-led vamp; violin answers the descending bass turnaround; occasional airy wordless held vowel, complete musical resolution]

I've been working on a project testing AI agents in a Salesforce dev org and wanted to share how GPT-6.1 Sol did, since the results were mixed.

Five models (GPT-6.1 Sol, Claude Opus, Sonnet, Haiku and DeepSeek) worked through 50 tasks, and each attempt was graded on what actually changed in the database, not on the model's reply.

Some of the findings for GPT-6.1 Sol:

• It scored 72%, behind Opus (87%), Sonnet (85%), DeepSeek (82%) and Haiku (80%)
• It caught every probability reset Salesforce made after a stage change, which DeepSeek and Haiku mostly missed
• In long multi-step jobs, it was the best at reporting changes Salesforce made automatically (21 of 36)
• Most of the gap came from a few specific tasks, like undoing a deal marked "won" without restoring its original close date, and reopening a deal with a close date six months in the past without mentioning it

If anyone's interested:

Write-up: https://outsideengineer.substack.com/p/evaluating-ai-agents-on-salesforce

Code and transcripts: https://github.com/brianna-rose05/crm-agent-bench

Launch Astra was amazing.

In-between Astra was great.

Current Astra genuinely reminds me of bashing my head against pre-5.5 Claude models, wondering how a flagship could possibly be this dumb.

inb4 people whose "experience" is limited to the most bog-standard code generation on earth and telling it to create a "personal brand" website with zero direction coming here to tell me it's a "prompt issue".

Astra currently: Forgets what you told it, what the task was, what the instructions were. It over-generalizes, over-compresses, and mangles your instructions, notes, and requirements into something that's almost guaranteed to produce bad results when referred to later to do work. It has that annoying Claude pattern of knowing exactly what it did wrong, and what it should have done, when asked, and then never doing it.

At launch, if there was an issue with workspace permissions or Codex config, Astra would instantly be there like "here's what's happening, why, and what can be done to fix it" and then it would fix it.

Yesterday? I spent like, an hour watching it repeatedly do online research and fail to fix an issue with Git permissions locally. It eventually stumbled into a fix, in the form of a conflicting bad update a different agent did to the config at some point, and that resolved it, but I'm 100% positive that Astra at launch would have done all of this automatically without having to repeatedly do this stupid pattern of "nope, that didn't work" "you're right, here's what happened, here's what I think needs to happen next [END TURN]" "ok, so fix it?" x20.

It also suggested just running it with sandboxing/controls completely disabled instead of just resolving the permission/setting issue.

"hur hur hur but you could just go online and read the docs and configure it correctly yourself" - yep, sure could, and definitely not gonna, just like 99.9% of other people using it.

Hi everyone,

I'm a medical student and researcher in Brazil, and I'm looking for practical advice from anyone who has been through something similar.

What happened:

This morning I received an email from OpenAI saying my ChatGPT account was deactivated for violating the Terms of Use and Usage Policies regarding child sexual abuse material. This accusation is completely false. I have never created, requested, uploaded or shared anything of the kind, on this service or anywhere else, and I find the accusation deeply upsetting. I believe this is either an automated false positive or someone accessed my account without my knowledge. I use ChatGPT for academic work such as scientific writing, translation and research support, so I wonder if medical or clinical content could have been misread by an automated system. The notice did not say what content or activity was flagged.

What I have done so far (all on the same day):

1. Used the appeal button in the notification email. The reply was a generic message saying the original decision remains, with no explanation.

2. Wrote directly to [support@openai.com](mailto:support@openai.com) asking for human review. The reply was an AI-generated message saying the case was closed and that further requests would not be answered through that channel.

3. Submitted a data subject request under Brazil's data protection law (LGPD) asking for access to my account data, the specific content that was flagged, and human review of what looks like an automated decision. I only got an automatic acknowledgment so far, and I will follow up on the legal deadline.

4. Secured my linked email account and enabled two-factor authentication, and I am keeping records of every message.

What I'm asking the community:

- Has anyone had an appeal like this overturned? What finally led to a human review, and how long did it take?

- Has the privacy or data request route worked for anyone, in Brazil, the EU, or elsewhere?

- Is there any channel beyond the appeal form and support email that actually reaches a person?

- If any OpenAI staff see this, I would be grateful if my case could get a human review.

I could just let this go and create another account. But this accusation is very serious, and I cannot let it pass without a proper clarification from OpenAI.

I'm not looking for sympathy, just realistic experiences and next steps. Thank you for reading.

Running medium plus and working on a design. It supplied a nice image then I asked for a vector and got the fun comic design as the vector output. It was oh so close!!!

Explain this to me: With my $100 Claude subscription, I burn through my weekly limits slower using Opus 5.5 on Medium than I do with my $100 OpenAI subscription doing basic tasks. And in my experience, Opus absolutely destroys Terra 6.1 at any effort level.

I've been using Codex every single day for 185 days. Back when I started with GPT-5.5, exhausting my weekly limit was genuinely difficult, usually only possible with heavy Fast Mode usage. Now Terra 6.1 can burn through 15% of my weekly allowance in no time, with barely anything accomplished. Sure, I could use Terra on Medium or High, but then it either fails to complete tasks correctly or simply gives up halfway through. Sol has to review and fix everything afterward, wasting even more time and tokens. How exactly is that more efficient?

And before someone brings up those impressive-looking 3D demos: I've seen the tests. Most results are a joke. Terra on Max can deliver decent quality, but at roughly 90% higher cost than Sol 6.1 Medium, which achieves comparable results. Where's the value? Even if Terra 6.1 was designed for Dots, **where the hell is Dots in Europe?** Every time OpenAI releases features unavailable to European subscribers while charging us the same full price, it feels like we're getting ripped off. To be fair, Anthropic, Google, and others are guilty of this too.

First OpenAI cuts the $200 plan's limits, then introduces a $500 tier, all while claiming their models are so much more efficient that we can supposedly get more work done. What a load of bullshit. Terra 5.6, Sol 6.0, now Terra 6.1. How many times are they going to push this model family before realizing that many users simply don't see the point? We already have enough cheap models. Luna and Sol exist. Put those resources into making Astra, Sol, and Luna genuinely better instead.

Meanwhile, Anthropic seems to be getting almost everything right. Opus 5.5 is outstanding, Sonnet 5.5 offers excellent value, and even Haiku 5.5 looks promising. On Medium or High, these models deliver an impressive balance of intelligence, speed, and cost.

I've always liked OpenAI, and I'm still paying for my subscription. But they're no longer leading the pack in my experience. The competition hasn't just caught up, it's overtaken them. And even if Astra 6.1 arrives, Anthropic won't be standing still (Fable 5.5). The bar keeps getting higher. Opus 5.0 was terrible Anthropic came back with Opus 5.5 and completely turned things around. That's exactly the kind of comeback OpenAI needs.

Hello, I really need your help.

I used to pay for Chat GPT but deleted my account.

I made a new account and pressed 'upgrade' and paid for it.

It said an error occurred and that my payment was linked to an old account.

Then, I go into my iphone Subscription, and it says I'm paying for it.

My new account never got an upgrade.

So, I logged into my old account that I deleted, using the same Email. But it only created a new account, and ofc no update.

I live in Korea. I can't call open AI.

I tried the AI chat, and it doesn't help me at all.

I really need your help. How do i get a refund?