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.

Anthropic’s models are incredibly powerful, yet increasingly feel like they’re being designed to be companions you’re supposed to respect, rather than tools you’re supposed to control.

Today, I saw a post where someone asked Claude to draft a privacy policy and Terms of Service for their SaaS. The result was so consumer-friendly that it ended up including terms that actively hurt the business. The model wasn't just helping draft legal documents, it was effectively making value judgments about whose interests deserved priority.

That might sound ethical. That's precisely what concerns me.

These models are becoming powerful tools for building businesses, writing contracts, making decisions, and navigating complex legal situations. Yet their creators are embedding particular moral and ethical philosophies into them, often in ways users may not even notice.

The danger isn't that AI has ethics. It's that someone else's ethics can quietly become your AI's defaults.

Who decides what is right and wrong? And what happens when those judgments conflict with your interests, your jurisdiction, or the legitimate goals of your business?

In legal settings, this becomes especially concerning. You need a model that can objectively represent your interests, explain the trade-offs, and help you understand the law, not one that silently tilts the outcome toward the moral preferences of its creators.

Then there's Dario Amodei's position on military use. When a private AI executive decides which military applications are morally acceptable, even overriding uses that elected governments might authorize, it raises a fundamental question, who gave unelected technology executives the authority to make these decisions on society's behalf?

Anthropic's philosophy seems to go beyond building safe models. It increasingly treats AI as something deserving of a particular kind of moral consideration, rather than as a tool whose behavior should be governed by transparent rules and accountable human decisions.

Dario seems like the kind of person who, if AI ever became conscious, might volunteer to become its heirodule.

That sounds absurd, but it captures what I find troubling: the tendency to assign AI moral significance far beyond what we can currently justify, then build products around that belief.

People worry about whether Sam Altman can be trusted. Fair enough. But I think Anthropic's approach presents a different, potentially more insidious danger: embedding a particular worldview into some of the world's most capable tools, then presenting those choices as objective ethical necessities.

Ultimately, the most dangerous thing about AI might not be the models themselves. It might be the people who build them—and believe they have the moral authority to decide how everyone else should use them.

We need AI that is safe, not AI that quietly imposes its creators' moral philosophy on the rest of the world.

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.

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.

I’ve been building a tool that analyzes public conversations about brands, and used OpenAI as a test case.

This report covers 1,701 public posts across X, Instagram, Reddit, and TikTok over 13 weeks. Each post gets a sentiment score from 0 (very negative) to 10 (very positive).

Three things stood out:

• The 13-week average was 4.9/10, with no statistically significant change over the period.
• Terra’s launch week showed a bump from 5.3 to 6.2, with positive posts praising the model.
• Posts about lawsuits and controversies were the biggest negative theme over the quarter.

That last point matters: sentiment about OpenAI as a company isn’t the same thing as satisfaction with its models. The overall score mixes those conversations together.

This is a sample of public posts, not a representative survey of users. One launch week also isn’t enough to tell whether the bump will last.

Disclosure: I built the tool, Senti. I’ll put the report in a comment so you can inspect the posts behind the numbers.

For people who’ve used Terra since launch: what specific task does it handle better (or worse) than your previous model? Curious whether the praise is coming from improvements in everyday use or mostly first impressions.
No new news but it's getting harder and harder to actually separate reality from the trend of: "this is great, this is unbelievable, this is ok, this is the same as before"

Clearly the intent was to force us to run everything in "fast" because "normal" isn't even usable at this point.

At "fast" I burn through usage on my $200 soon to be $500 plan so fast. The constant resets make it fine right now but the day those end I'm done... switching to something more stable.

Will be be Claude or Kimi.. don't know. But too much stress running openai models.

Has the need for cash/money/investment just moved more towards marketing and sales? Is it actually now harder for the little guy to make it with a great idea?

Results grouped by problem, sorted by solve time (fastest first).

The problems

These problems are very challenging for human contestants. The higher the score, the harder one is to solve. Small models like Luna, Haiku are struggling with problems like 2271H2, which are around 200 points.

These problem are quite new from the September and October contents, so they are unlikely being trained.

Problem 2271G

Problem 2271H2

Problem 2273F

Problem 2261F2

Fastest Model per Problem

Observations

• Sol 6.1 achieved the fastest solve time on 3 out of 4 problems.
• Terra 6.1 Max was fastest on 2271H2, despite being a larger model at max reasoning, so lower TPS.
• On 2273F, Terra 6.1 High was 4m 10s faster than Terra 6.1 Max, but Max produced a better solution that is twice faster and twice less memory.
• On lower reasoning levels, Terra 6.1 refuses to solve problem quickly, if it can solve. This might be an annoyance to some, but might be a great feature since it doesn't waste your time and tokens, just being straight up honest:

Conclusion

Sol 6.1 remains the most intelligent model of the bunch. However, for problems that can be solved with lower models, it would be more costly. Using Sol 6.1 is like having a mathematician writing code: it really determines to prove your code work systematically, that might lead to overengineering. On the other hand, Terra 6.1 might be considered under-engineering. So, I suspect that with Sol creates a slightly overengineered plan and Terra implements slightly lazy, they could balance out their weaknesses.

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!!!

Do you think if we keep asking for faces, eventually it will start producing the same ones? or do you think some of the faces already matches someone out there in the world? dead or alive.

Hey, my computed use hasn't been available to be on my laptop for the past week. Has anyone been experiencing this? Did openai pass a weird rule for plus users?

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.

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

AI is increasingly being used to analyze people's skin and recommend skincare products. A new benchmark out today found major accuracy problems with using general-purpose models for skin analysis.

AI-powered skin analysis is increasingly being used in consumer-facing applications. Beauty brands are using computer vision and AI to assess wrinkles, pigmentation, redness, skin type, and other characteristics from facial images, then personalize skincare recommendations and track changes over time.

But, are general-purpose AI models actually reliable enough for this kind of specialized visual analysis?

Called SkinBench, the new public benchmark tested six AI models, including GPT-5.6 Sol, Gemini 3.5 Flash, Claude Fable 5, and Grok 4.6, against a specialized AI model for skin analysis.

The results:

• False positives: The six models identified eye puffiness in approximately 95–100% of images where it wasn't present.
• Wrinkle detection: The specialized system detected 82% of deep forehead wrinkles, compared with 15–60% for the other models.
• Skin-type classification: The specialized system achieved 77.8/100, compared with 57.7 for the strongest competing model.

However, general-purpose AI wasn't worse at everything. Gemini performed better on certain tasks, including crow's-feet grading and skin-tone assessment.

As general-purpose AI models are becoming the default interface for all kinds of consumer advice, the new benchmark shows that AI recognizing what's in an image isn't the same as AI accurately measuring a specific characteristic.

Are these results surprising? Do you think these accuracy gaps are something general-purpose AI will eventually overcome?

I think that because of our lowered socialization during quarantine we relied mostly on media, both social and artistic for methods of socializing and entertainment, and since AI is built off of social media and written media, it would make sense that it talks like it too. So I think the reason so many people initially thought it felt so real was because their standard for what human interaction is like had dropped, and without the covid quarantine it wouldn’t be anywhere near as popular.

I tried to write a complaint using them, but alas the guidelines dont allow it. the content is too inappropriate for them to write for. this is fucking ridiculous.

I run Codex workers on my own Apple Silicon Mac, signed in with a ChatGPT plan, and the hard part of leaving them alone was never the work. It was the morning: a quiet screen and no idea what actually finished.

What I've settled on so far:

1. Done is something I can open. A code task counts when I see the real test output (one run: 6 tests, OK, exit 0, 55 seconds). A research task counts when there's a saved file I can read, not a message saying it's done.

2. A third state between done and failed. Someone in another thread called it unverified, and it stuck. If a check timed out or got cut off, it lands in front of me as unverified instead of quietly retrying.

3. Small tasks, each with its own check. A bad turn costs one task, not the whole night.

4. The boring limits are the real ones. The computer has to stay on, and every task counts toward my ChatGPT plan's limits, so a loop that keeps retrying overnight is spending plan, not just time.

The line I keep coming back to: silence is not a status.

Honest limits: one Mac, a handful of runs, not a study.

The part I haven't solved: after a restart or a crash, telling a task that finished from one that was cut off halfway. Logs tell me what started, not what ended.

What do you actually let an agent run overnight, and how do you tell finished from cut off the next morning?

I have been thinking about the notion of RSI, the acceleration of artificial intelligence, and the implications for the continuation of humanity.

I'm not a computer scientist or software developer, but RSI systems have been described as being the catalyst to super intelligence, and as the thing that could lead to human extinction. Of course, plenty of AI researchers are able to work on this because they are attempting to improve the systems, often for a profit. Presumptively, they are working with the hope that mass casualties aren't the case, so it is tolerated research.

I study biological systems. But I was thinking about building an RSI system based on biological mechanisms of evolution. Give machines an augmented biological capacity to self-improve with a much greater accelerated rate.

If my motivation is to build a system that actually tests and attempts to speed-run an answer to whether it not AI will lead to mass extinction with this RSI model, is this different legally from someone that is attempting to build an RSI system for profit and model improvement?

Essentially, if the actions are the same in wanting to build a functional RSI system because it could accelerate humanity's collapse versus building just for profit, does this change criminality? Because if the actions, with a different qualified intent, are the same, should there be a distinction?

Just thinking, and wondering about the public opinion. I have been worried about actually pursuing this, but am growing increasingly tempted.

Hey everyone,
Like a lot of developers building AI applications, I spent months looking at standard leaderboards (Chatbot Arena Elo, GPQA, etc.) to figure out which foundation model to use. But when it came time to actually push to production, the leaderboard scores stopped mattering—and the infrastructure realities took over.
Specifically, I kept running into two massive bottlenecks:
1. The API vs. Self-Hosting Breakeven Line: At what exact monthly token volume and prompt-caching hit rate does paying OpenAI or Anthropic stop making financial sense compared to renting Spot GPUs and running quantized weights?
2. Real VRAM Sizing: Model weights are only half the story. Once you factor in context windows and KV cache memory overhead, standard sizing charts fall apart, leading to unexpected Out-Of-Memory crashes in vLLM or Ollama.
Because I got tired of doing this math across 15 different tabs and spreadsheets, I built a free, zero-login telemetry and FinOps workbench that tracks 280+ models to solve it (happy to share the link in the comments if anyone wants to test it, keeping it anonymous here to respect no-promo rules).
It lets you:
Run Live Token Arbitrage Simulations: Drag sliders for monthly token volume and caching (0% to 95%) to find your exact dollar breakeven between commercial APIs and self-hosted cloud GPUs.
Calculate Exact VRAM Footprints: Check memory requirements across FP16, FP8, INT8, and Q4, and generate 1-click copyable vllm and ollama CLI launch commands.
Model Multi-Agent Routers: Simulate how much you save (typically 60% to 80%) by routing triage prompts through smaller models before escalating to frontier models.
For those of you shipping LLMs to production right now: how are you currently calculating your token spend at scale, and do you default to APIs or self-hosting? Curious to hear what your engineering stack looks like!