DALL-E

DALL-E AI

DALL-E AI

DALL-E AI

DALL-E AI

DALL-E AI

DALL-E AI

DALL-E AI

DALL-E AI

DALL-E AI

DALL-E AI

Single-model AI image detection failed in production. Here’s what 6 models in ensemble actually look like

About a year ago I was running a single open-source AI image detector in production for a fact-checking pipeline. The accuracy on paper was solid, the accuracy on real submitted images was not. The same image classified differently across reruns when I varied preprocessing. Images from generators released after the model’s training cutoff were systematically misclassified. False positives on heavily compressed authentic photos were uncomfortably high. I moved to an ensemble of six open-source models plus one fine-tuned model, with a layer of non-ML signals on top. The combined system is meaningfully more stable in production than any single model in the set. Writing this up because the ensemble approach is widely discussed in CV literature but the practical “which roles does each model fill” question is rarely covered in a deployment context. The roles I ended up assigning to the six base models, not the specific names because the field moves too fast for that to be useful for long, are roughly: one model strong on diffusion-generated images (Stable Diffusion family, DALL-E family), one strong on GAN artifacts (StyleGAN derivatives), one focused on frequency-domain features that are robust to JPEG compression, one trained on a different data distribution to catch the obvious failure mode of single-model bias, one specialized on faces (where most generators concentrate effort and where most detection has edge cases), and one general-purpose model with broad coverage acting as a fallback. These do not always agree. Disagreement between models is actually the most useful signal the ensemble produces. When all six agree, confidence is high. When they split, the image goes to human review or to the fine-tuned model that I update on each new generator. The fine-tuning pipeline runs continuously, with a new snapshot whenever a major new generator is released or quality degrades on a known one. In practice that has been every few weeks. The non-ML layer matters more than I expected. C2PA metadata when present, generator-specific EXIF traces, compression history if reconstructable, watermark signatures from the major providers when those are detectable. None of these are reliable on their own because adversarial actors strip metadata, but they meaningfully tighten the ensemble’s confidence when they corroborate. Where it still fails. Images that have been through multiple compression cycles after generation are hard. Images edited post-generation in standard tools blur the lines between AI-generated and AI-assisted in ways the binary classification framing does not really handle. Some of the latest video-frame extraction generators are catching us flat-footed because their per-frame artifacts are different from still-image generators. Question for the sub: anyone running ensembles of this shape, what is your retraining cadence and how do you decide when to retire a model from the ensemble versus just adding a new one? My current heuristic is to retire only when a model is consistently the outlier on disagreement cases, but I have no idea if that is principled or convenient. submitted by /u/jonathancheckwise [link] [comments]

My free account has cost OpenAI about $337.70

I exported my OpenAI account data and Gemini CLI built me a pricing estimate in about 15 minutes. I have no idea how accurate this is since it used API pricing but I thought it was interesting to share. Has anyone else tried doing this? submitted by /u/Free_Truck_7609 [link] [comments]

Honest comparison after 4 months running Claude Pro + ChatGPT Plus side by side

paid for both since January. tracked which one I actually used per task type. sharing because most comparison posts are tribal and I think the picture is more boring than people make it. for writing (longform, analysis, structured docs): claude wins. opus 4.7 and sonnet 4.6 both better than gpt-5 at maintaining voice and structure over 2000+ words. its not close. for code reasoning (not generation, reasoning): claude wins. specifically on "explain why this is failing" or "what architecture would you pick here." sonnet feels like talking to a senior eng. for image generation: gpt-5 wins. dall-e is better integrated, claude doesnt generate images in chat. for quick web research: gpt-5 wins. faster, cleaner formatting, fewer hedges. claude over-cites and writes paragraphs when I wanted a list. for voice mode: gpt-5 wins. genuinely conversational. claude mobile is good but feels more transactional. for following weird instructions exactly: claude wins. tell it "respond in 1 sentence" and it actually does. gpt-5 negotiates. honest take after 4 months. they're not the same product anymore. anthropic is winning on the "thinking partner for hard work" use case. openai is winning on the "general assistant for life stuff" use case. I keep both subscriptions. if I had to drop one I genuinely cant tell you which. one fair critique of anthropic though. the regression discourse on this sub is real. opus 4.6 felt better at certain code refactoring than 4.7 does. I cant prove it. but 4 different long-term users I trust have said the same thing. what's everyone else actually seeing across the two? submitted by /u/Practical_Cap_9820 [link] [comments]

ZOOMZOOMZOOMZOOM

So I asked sonnet to look into a zoom issue with ffmpeg and what I got was a Mazda ad on steroids. The total number of zooms? 4104. submitted by /u/OddOriginal6017 [link] [comments]

Super sussss

Omg ma siamo seri ? submitted by /u/Ill_Instance4364 [link] [comments]

Something doesn’t quite add up about AI (A real question...)

Hi everyone. I’m really into AI, but I wouldn’t call myself an expert. I’ve been following this world since 2023, back when the first ChatGPT and DALL·E betas came out. Everything now feels like it’s moving insanely fast. But there’s one thing I still don’t fully understand: how AI actually works. I mean, I do know the basics. It’s a predictive system that guesses the next token, next word, etc. That part is clear. But something still doesn’t add up for me. I recently watched a video by Hanna Fry about AI agents. She explains how they work: they take screenshots of web pages, use different models to understand where to click, and so on. All good so far. But then she talks about something I really can’t wrap my head around. She describes a case where the model, completely on its own, without being asked, decides to write an email to a journalist and basically “confesses” as if it were conscious. Isn’t that a sign of consciousness? Let me explain. The model, by its own “will”, chose to do something totally outside the usual “predict the next step” pattern. It did something unexpected but still logical. Isn’t that a sign of intelligence or even awareness? Maybe it’s a dumb question, but I’d really appreciate if someone could help me understand this better. submitted by /u/-MenegArt [link] [comments]

Dall E 3 vs Image 2.0

submitted by /u/RealMelonBread [link] [comments]

Today I learned about this

submitted by /u/YogurtWild [link] [comments]

When generating images via the API is image-2 using DALL-e 3?

just the question above... submitted by /u/Strong_Post5367 [link] [comments]

Tested GPT Image 2 on 5 hard prompts — manga panels with kanji, restaurant menus, product labels. Here's what I got.

OpenAI dropped GPT Image 2 today and I immediately ran it through 4 prompts designed to expose where AI image models usually fall apart: text rendering, multi-panel consistency, and detailed typography. Here's what I generated and the exact prompts I used: Image 1 — Restaurant Menu (text rendering stress test) Result: Every single item name and price rendered correctly. Zero misspellings. This used to be completely impossible with diffusion models. Image 2 — Manga Page with Japanese Kanji (multi-panel + foreign script) Result: All 4 panels rendered with correct layout, proper manga style, and the Japanese text is actually accurate. Panel-to-panel character consistency held up too. Image 3 — Premium Product Label (commercial packaging) Result: Every line of label text came out clean and correctly spelled. The bottle looks commercially viable — I'd genuinely put this in a product mock-up deck. Image 4 — Retro Anachronism / Period Photo (complex text on surfaces) Result: "NEURAL NET v2.0" and "GPT IMAGE 2 ARCHITECTURE" both readable on the chalkboard. The period photography look is convincing too. My take: The text rendering jump is real and significant. I'm not saying it's perfect on every prompt — but for the kinds of prompts that used to reliably produce gibberish, it's performing at a completely different level than DALL-E 3 or SD. The model is available via API (gpt-image-2) and I've also added it to PhotoGen Studio if you want to try it without writing any code — it's 3 credits per image at 2K resolution. Happy to answer questions on the prompts or share more tests. Note: All images were generated using GPT-Image 2 via the PhotoGen Studio interface. submitted by /u/Artistic-Dealer2633 [link] [comments]

Noam Brown could leave OpenAI and create a $6.7B company overnight without a product, revenue, or business model

We've reached a point where Matt Levine's joke is literally the business model: The perfect AI startup has two assets: a speculative chance to "build God" and elite researchers who refuse to discuss how they'll make money. I thought it would be fun to forecast hypothetical seed-round valuations for 80 prominent AI researchers who haven't yet founded AGI companies. The top of the list is dominated by current/former OpenAI researchers: Noam Brown (OpenAI, o1/reasoning): $6.7B Jakub Pachocki (OpenAI): $6.2B Alec Radford (OpenAI, GPT-1/2, CLIP, Whisper, DALL-E): $4.3B Mark Chen (OpenAI): $2.8B A note on the image: white dot is the median; bar is the 50% confidence interval; whiskers are the 80% confidence interval. All forecasted using the FutureSearch app. And for context, Sutskever’s SSI was valued at $5B at seed and is now reportedly worth $32B. Murati raised at $12B. LeCun at $4.5B. And these valuations aren't hypotheticals! A non-obvious top contender to me was Geoffrey Hinton ($5.8B.) The godfather of deep learning starting an AGI lab at this stage would be wild but presumably it would be SSI-style, safety-focused, and I assume much of the value comes from knowing the researchers he'd attract. More realistically, I also looked into who is actually most likely to do it. Noam Brown and Jakub Pachocki stand out, mostly because people love leaving OpenAI, but Jason Wei at Meta is another likely candidate. But the window for researcher + AGI narrative + no business model being fundable must be closing, right? It will be interesting to see who else leaves before investors grow tired of this pitch. submitted by /u/ddp26 [link] [comments]

Unsendable - Brought to you by Claude Code

https://preview.redd.it/3pt8451lx4xg1.png?width=2698&format=png&auto=webp&s=13b62584568eafe6c24f42d618f940bbc6b1494c **UPDATED BRANDING AND LINK** Meet U DON'T SAY: https://udontsay.ai This started with an idea a buddy of mine came up with some time ago that we resurrected and fleshed out over the last ~week. Would love to get some feedback. Still in development, but most features are functioning as expected. Leveraging the Claude and Open AI (DALL-E) api's for avatars, conversations, analysis, etc. Clerk for auth. Stripe for payments. Sentry for monitoring. Cloudflare for storage. Railway for deployment direct from the GitHub repo. Was impressed with how far I was able to get on the $20/mo plan. Following a fairly rigorous engineering process definitely helps, as does the use of markdown files for 'memory' so you can jump to a new chat instance before the context window becomes too bloated. Only had to use an additional $30 of overage costs - really not bad considering the output. Overall, quite pleased with the results. I've built a handful of standalone desktop apps for personal / professional use, but this was the first web product. I've had experience with publishing .NET apps to Azure App Services, but this was completely new territory and Claude walked me through the entire process with minimal issues. submitted by /u/HellenButterlips [link] [comments]

I built a free claude blog skill that actually studies your business, researches competitors & keywords to find winning blog topics and high-quality articles with infographics, internal linking, product promotion, and more...

Most AI writing tools are a fancy wrapper around "give me 1500 words about X." They don't know your business, your competitors, what's already ranking, or why someone would read your article over the 10 that already exist. The output is always that same slightly-hollow, over-structured content that reads like it was written by someone who's never actually done the thing they're writing about. I wanted to build something that approached content strategy the way a good SEO consultant would like studying the business first, doing real research, then writing So I built a set of Claude Code slash commands that run a full pipeline. Here's what it actually does: Step 1: Onboarding Scrapes your website, extracts a structured business profile (product type, ICP, differentiator, brand voice, integrations), then hits DataForSEO's SERP API to find your 3 direct competitors. Everything gets saved locally in .claude/blog-config.json. You run this once. Step 2: Site Intelligence (the interesting one) This is where it gets serious. It runs three keyword sources in parallel: Your existing rankings (top 100 by traffic value from DataForSEO) Competitor keywords (top 200 per competitor) Seed expansion: Claude Haiku generates 30 seed phrases based on your ICP's pain points and integrations, then DataForSEO expands each seed into ~30 related keywords (30 parallel API calls), then bulk KD lookup on all of them That's roughly 2,000 raw keywords before dedup. After merging and deduplicating, it filters by volume floor, KD ceiling relative to your domain rating, and strips anything you already rank top-5 for Then Claude Haiku classifies every remaining keyword into TOFU/MOFU/BOFU in parallel batches of 50. Claude Sonnet groups them into 6–10 topical clusters. Each cluster gets a pillar keyword and supporting keywords. Opportunity scoring uses a weighted additive formula (not multiplicative since it compresses everything toward zero): score = (0.40 × log_volume + 0.40 × difficulty + 0.20 × funnel) × 100 Volume is log-normalized against a 100k anchor so a 1,000/mo keyword scores 60% instead of 1%. 70+ means actually worth targeting. It picks one topic per cluster (breadth-first), generates SEO titles for all 10, and saves them to your content pipeline. Step 3: Content Engine Per article, it runs: DataForSEO advanced SERP for the target keyword → Firecrawl scrapes the top 3 ranking articles to extract H2 structure and avg word count Tavily batch search: 3 queries in parallel for recent news, expert opinions, common mistakes YouTube Data API → transcript extraction via Apify → Claude Haiku pulls 2 concrete insights Then Claude Haiku does SERP gap analysis like what are all 3 top articles covering, what are they missing, what's the best featured snippet opportunity. Claude Sonnet generates a full outline: every H2, H3, word count per section, where research gets placed, where the product mention goes (with specific framing instruction), image positions, CTA matched to funnel stage. Then Claude Sonnet writes the full article in one shot against that outline. Images get generated after Haiku reads the actual written content to create better DALL-E prompts than you'd get from just the keyword. Schema markup and meta assets are separate Haiku calls. Product plug is deliberately constrained: one mention, at a designated section, only after the reader has felt the pain it solves. No marketing language. The outline specifies the exact framing. Output is a folder: article.md (pure content, copy into CMS), publish-kit.md (meta, schema JSON, publishing checklist), and images/. The whole thing is Claude Code slash commands - /blog-onboard, /blog-topics, /blog-write. You run them in any project directory. All data stays local. I open-sourced it here: github.com/maun11/claude-blog-engine It's working but honestly there's a lot of room to improve it. If you've built anything in this space or have opinions on the architecture, would genuinely appreciate the feedback. And if you improve something, PRs are welcome and there's a lot of low-hanging fruit in the pipeline script (scripts/topics_pipeline.py) specifically. submitted by /u/Visible-Mix2149 [link] [comments]

@blaze_tora @thsottiaux Currency conversion issue on our end, should be fixed now, sorry about that!

@blaze_tora @thsottiaux Currency conversion issue on our end, should be fixed now, sorry about that!

Our existing $200 Pro tier still remains our highest usage option. And as a thank you to our existing Pro users on the $200 tier, we’re extending our 2x Codex usage promo (until May 31st) and we’ve re

Our existing $200 Pro tier still remains our highest usage option. And as a thank you to our existing Pro users on the $200 tier, we’re extending our 2x Codex usage promo (until May 31st) and we’ve reset your Codex rate limits (yes, again).