PH热榜 | 2026-04-26

GPT-5.5 feels like a real shift toward agentic AI 🤯

It introduces a new class of agentic AI designed to execute complex, multi-step tasks autonomously instead of just assisting. It solves the core limitation of LLMs: needing constant human steering for real work.

What makes it different?

• Agentic workflow execution (plan → tool use → verify → iterate)

• Maintains long context across systems & tasks

• Higher intelligence without latency tradeoff* (matches GPT-5.4 speed)

• More token-efficient → better outputs at lower compute cost

• Stronger autonomy in ambiguous, real-world scenarios

Key technical capabilities

• State-of-the-art coding performance (Terminal-Bench: 82.7%)

• Advanced tool usage & computer operation (OSWorld: 78.7%)

• Long-context reasoning up to 1M tokens (API)

• End-to-end SWE task solving (SWE-Bench Pro: 58.6%)

• Knowledge work benchmarks (GDPval: 84.9%)

• High-performance agent workflows (Tau2 Telecom: 98%)

Features

• Agentic coding (debugging, refactoring, testing, validation)

• Autonomous research & analysis loops

• Spreadsheet + document generation

• Cross-tool navigation (browser, software, APIs)

• Scientific reasoning & multi-step data analysis

• Built-in safety systems + cyber safeguards

Availability

• Available in @ChatGPT by OpenAI (Plus, Pro, Business, Enterprise)

• Integrated deeply into Codex (CLI, IDEs, web, app) for agentic coding workflows

• API access (Responses & Chat Completions) coming soon with up to 1M context

Benefits

• Ship features faster (hours instead of days)

• Reduce debugging & iteration cycles

• Automate complex workflows end-to-end

• Higher quality outputs with fewer retries

Who it’s for & use cases: Developers, data scientists, researchers, startups, and enterprises for building full-stack apps, debugging large codebases, automating workflows, financial modeling, and advanced research analysis.

This isn’t just a better model, it’s a shift toward AI that can actually operate like a teammate across ChatGPT and Codex.

P.S. I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified → @rohanrecommends

Finally took the opportunity to test Codex, as I am apprehensive about moving from Claude Code.

I am taking the opposite approach and having Codex do the thinking as it is faster, seems strange but it's good for things like:

• Check my repo for any deployment exposure.

• Please review my observability dashboards, what are they telling me?

• Review my sales website, what are the 3 highest ROI gaps worth closing now?

Still haven't allowed Codex to touch my code.

Can confirm: has officially dethroned Claude Opus 4.7

AI assistants have gotten good at work tasks. The gap has always been everything else: booking dinner, ordering groceries, finding a trail, filing taxes.

What it is: Claude Connectors expands Claude's app directory to 200+ services, now including everyday tools like Instacart, Spotify, AllTrails, Resy, TurboTax, Uber, and TripAdvisor.

Most AI tools stop at the edge of your work. The rest of your day still happens across a dozen separate apps, with no shared context between them. Claude now acts inside these apps from within a single conversation. It surfaces the right connector for what you are doing, lets you refine across multiple services in the same thread, and carries your context throughout.

What makes it different: When two connectors could help, you see both and choose. Your data from connected apps is not used to train Anthropic's models. Before Claude books or purchases anything on your behalf, it confirms with you first.

Key features:

• 200+ connectors spanning work and everyday life

• Dynamic suggestions: Claude surfaces the relevant app inside the conversation

• Multi-connector threads: pull from several services without switching context

Benefits:

• Plan a trip, build a grocery list, and book a table in one conversation

• Context carries across connectors so you are not re-explaining yourself each time

• Available on all plans, web and desktop now, mobile in beta

Who it's for: Claude users who want one assistant across their whole week, not just their working hours. The consumer connector expansion is the obvious missing piece for Claude becoming a general-purpose personal layer.

P.S. I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified → @rohanrecommends

I can see some for travelling – just in time when I want to conduct a family trip! :D

Has anyone had success in adopting connectors at their place of employment?

If so how did you address, InfoSec/Privacy/Audit challenges?

Struggling to gain traction/support for opening up connectors with CoWork.

Finally! I am going to use some of these for my upcoming trip!

The idea is interesting, but half of the connectors (Instacart, Resy, TaskRabbit, Uber Eats) don't work outside the US. I'd like to see something for the rest of the world – Booking and Spotify are good, but not enough.

This is genuinely game-changing! I've been using Claude daily for our app launch (analytics, strategy, content) and having to constantly switch between tools breaks the flow.

The Spotify + Instacart + Uber integration is interesting, but I'm really curious about business app connectors - things like Asana, Slack, Google Drive. would love to see those part of the 200+ to keep project management in the same conversation as strategic planning.

Hey Product Hunt, Rebekka here, co-founder at Trismik 👋

We built QuickCompare because we kept seeing the same pattern: teams shipping LLM features were making model decisions with surprisingly little evidence.

Often, they were defaulting to the biggest or most familiar models, relying on public benchmarks, or testing a couple manually and calling it a day. But in practice, that can mean spending far more than necessary on inference without actually getting the best result for your use case.

The reality is that model choice is rarely one-dimensional. It is not just about which model performs best. It is also:

• Which model performs best on your prompts and tasks?
• Where can you cut inference cost without sacrificing output quality?
• When do cheaper models actually match or outperform the expensive default?
• How do cost, speed, and task performance trade off side by side?

For many teams, especially those building AI products at scale, this has real business impact. Huge monthly inference bills, slow experimentation, and too much guesswork in a decision that directly affects margins, product experience, and speed to market.

That is why we built QuickCompare.

QuickCompare helps teams compare models on their own data, side by side, across quality, cost, and speed, so they can make a confident decision based on their actual use case, not generic benchmarks.

And we also built Ziggy, our AI Scientist assistant, to make this much easier. You don't need deep evals expertise to get started. Ziggy helps you set up and run comparisons in a much more intuitive, no-code way.

The goal is simple: help teams find the right model for the job, often cutting cost dramatically while maintaining or even improving performance and speed.

If you're building with LLMs, we'd really love your feedback. In particular, I would love to hear:

• how you are choosing models today
• whether inference cost is a major pain point for you
• what makes model evaluation feel slow, difficult, or inaccessible in practice

🔗 Try QuickCompare free: We’d love to hear what you think 🙏

🎁 Product Hunt bonus: Get an extra $10 in free QuickCompare credits

Thanks so much for checking us out and supporting the launch!

Hi Product Hunt, I’m excited to share that our Cambridge spinout, Trismik, is launching QuickCompare today.

We built QuickCompare to help AI teams compare LLMs on their own tasks and data, so they can make better decisions before deployment, fine-tuning, or migration.

As both a Cambridge academic and a co-founder, I’ve seen how difficult model selection can be in practice. There’s no shortage of models, but there is still a real need for fast, practical evaluation on real world use cases.

QuickCompare is for teams asking:

• Which model performs best for our workflow?

• Which model is most reliable?

• Which model is worth deeper investment?

Thank you for taking a look, we’d really love your feedback!

Nigel, co-founder of Trismik

Excited to hunt QuickCompare today.

QuickCompare helps teams choose the right LLM based on how models actually perform on their own data. Not generic benchmarks.

You upload your dataset, select the models, and get a side-by-side view of performance, cost, and speed.

What stands out here:
• Real evaluations on your own prompts and use case
• 50+ models compared in a single workflow
• Clear trade-offs between quality, cost, and speed.
• No manual scripts or ad-hoc testing

If you’re building with LLMs and tired of guessing which model to use, this is definitely worth checking out.

Great launch! Btw, can I compare models for specific tasks like marketing, coding, or support?

Hi Product Hunt! I'm Alice, on the Science team at Trismik. Big day for us today, and I've been looking forward to sharing what the whole team has built.

The shape of QuickCompare is a four-step flow: bring a dataset, configure how you want to evaluate (which metrics, which columns, optionally an LLM-as-Judge), pick the models you want to compare, then run them all in parallel against your data. What you get back is a side-by-side view of accuracy, inference cost, and average latency for each model, plus a breakdown of how each one performs on the easier vs harder slices of your dataset rather than just the headline average. The "your dataset" bit is the point: it's an LLM evaluation tool that scores models against your actual task, which we think is what makes it a more useful LLM Arena alternative for teams who have their own data and need a real answer rather than a popularity vote.

Ziggy is the AI assistant inside QuickCompare, and the part I've spent most of my time on. It exists because most LLM evaluation tooling assumes you already know your way around prompt templates, judge prompts, and which metric makes sense for which task. That's a pretty steep tax for someone who just wants to know which model is cheapest at acceptable quality on their data.

So Ziggy looks at your dataset, suggests sensible columns and metrics, writes the Jinja2 input template, and if you need an LLM-as-Judge setup it drafts the judge prompt with a scale that fits the task (binary for classification, 1 to 5 for open-ended generation). You can chat with it the whole way through and it knows where you are in QuickCompare, what you've already filled in, and what's still missing. Once the run finishes, it switches into analysis mode and helps you interpret the cost, latency, and accuracy numbers across the models you ran.

Has anyone had a model you assumed was the right call turn out not to be once you actually tested it? That gap is the bit I find most interesting and would love to hear stories.

Thanks so much for supporting us today, and do give QuickCompare (and Ziggy!) a try 🙂

Hey Rebekka! It's awesome. I usually deal with understanding which LLM is the best option for a given case and it's taking a lot of time. I'm sure it's gonna change the game. Wish you all the best here!

Great launch! 🚀 Have to try it out for optimizing our LLM based flows at CatDoes.

Interesting! This can be really useful for our research team. Our pipelines are a mix of different model usually.

ive been running gpt-4o-mini in production for sentiment scoring on my crypto site, and the question i actually care about is "would switching to claude haiku save me money without tanking quality on MY prompts". Does quickcompare measure cost per call and tail latency alongside output quality, or just output quality ?

QQ: who's the buyer?

As a solo founder I picked one model and stuck with it because the switching cost (prompt re-tuning, eval

rewriting) usually outweighs the marginal gain.

Comparing 50 LLMs feels like enterprise eval-engineer kit.

What's the indie use case I'm missing?

I have only today adopted Codex, partially into my stack, just because Claude is too slow for some things.

You gotta check this out if only for the onboarding...! So fun!

Question for Chris:

What made you go fully native instead of Electron?

Asking because the entire category (FontBase, RightFont, etc) is cross-platform and slow, and a native-only positioning is either brave or a deliberate moat?

Sad that I can't use it on Sequoia, but great product and great launch!

Happenstance is an AI people-search layer that runs across your Gmail, calendar, Twitter, Instagram, and other connected accounts using natural language queries.

Problem → Solution: Your real network is scattered and effectively unsearchable. Some contacts are buried in a 2019 email thread. Some are Twitter mutuals you've never properly spoken to. Some are conference attendees from three years ago. Happenstance connects all of it and lets you describe who you need the AI handles the search across every account you've linked.

What makes it different: The friend-sharing model turns a personal search tool into a collective one. Share your network with trusted contacts, and your search radius expands to include their connections, too warm by definition, not cold by default.

Key features:

• Natural language search, no filters to configure

• Multi-network: Gmail, Calendar, Twitter, Instagram, and more

• Friend and group sharing to extend reach across trusted networks

• MCP, Claude, Claude Code, ChatGPT, and Slack integrations

Benefits:

• Skip cold outreach entirely for sales, hiring, and fundraising

• Surface candidates through referrals before a job goes public

• Find investor intros through connections you already have

• Runs inside your existing workflow via native integrations

Who it's for: Pre-seed founders, AEs, recruiters, and investors who know the right path to anyone starts with who you already know. 300,000 users and teams at Perplexity, YC, Greylock, and Brex are already on it.

The network effect is real.

P.S. I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified → @rohanrecommends

the warm vs cold distinction is interesting but what i'm more curious about is the false negative problem - how often does it surface someone you completely forgot you had a connection to vs how often does it miss someone who should have shown up? for a tool selling on "skip cold outreach entirely" that precision gap seems like the whole game

Solo founder here doing the outreach grind right now (reddit, x, producthunt all of it) and the bit i'd actually want from a tool like this is honesty about how warm the warm connection really is. If i emailed someone 3 years ago they're not warm anymore, just searchable. Does happenstance try to weight by recency or recent interactions, or is everyone whos ever been in your inbox a "warm intro" candidate ?

Congrats @sachamorard , did some quick market analysis for Edgee: https://www.ideajarvis.ai/idea-posts/ddbf4b65-76ed-46dc-978a-e3b656eb7109

one idea: flip the leaderboard from "biggest token spender" to tokens-per-merged-PR. You already have the GitHub attribution and the compression-adjusted token counts in one place, so joining them is mostly UX work. The reframe is bigger than it sounds though — cost dashboards are observability, but tokens-per-PR is actual AI engineering productivity. It's also a much better pitch upgrade for CTOs: "where did our budget go" is interesting, but "who ships the most with the least" is what they'd actually want to know.

Hey Product Hunt 👋

I’m launching a free chart generator from Embedful.

You can upload a CSV or Excel file, instantly turn it into a clean chart, and export it as SVG, PNG, or PDF.

No signup. No complicated setup. Just fast, simple charts you can drop straight into reports and documents.

Would love your feedback 🙏

Hey Fernan,

Was interested at taking a look at your site but I was greeted with a lot of errors due to my VPN/Malware blocker (just FYI).

I like some of the charts, I think the more esoteric charts excited me the most, I particularly like tree-maps and radar charts.

👋 Hey Product Hunt! Mohamed here, founder of Veritads.

I built this after noticing something in the livestreaming world. Streamers were paying people to clip their VODs and post them on TikTok and Instagram. Views kept coming in for months after they paid. Just real people, posting on their own accounts.

I thought: why isn't every brand doing this?

Paid ads stop the second your budget runs out. Influencer deals cost thousands with zero guarantee of results. People's brains have been trained to skip anything that looks like an ad. Veritads is different. Real people create and post short-form content for your brand from their own accounts. It doesn't look like an ad because it isn't one. You only pay for verified views. The content stays up forever.

It works for any brand, streamer, artist or startup that needs organic reach without a massive ad budget or a creative team.

🎁 Product Hunt exclusive: launch your first campaign this month and we'll add 10% on top of your budget, on us.

This is a beta launch. I'd genuinely love your feedback. What's confusing, what's missing, what you'd want to see before trusting a platform like this with your brand's growth.

Happy to answer everything in the comments.

Hi Hunters, I taught AI coding agents to speak human.
AI can ship code in minutes. But the handoff is still a wall of text.

LLMs are verbose by default.
They repeat, over-explain, and burn tokens on filler.
That means slower responses, faster usage limits, and more “wait, what changed?” moments.

Layman fixes that.
It makes your agent output clear, short, and useful — while cutting the fluff.

What stands out

• 🧠 Plain-English handoffs: updates your whole team can understand fast

• ✂️ Up to 75% fewer output tokens in brief modes (same core meaning, less noise)

• ⚡ Faster responses: fewer tokens to generate = quicker answers

• 🎚️ Multiple modes: Summary, Explain, Lite, Full, Ultra, Wenyan

• 📝 Better delivery tools: layman-commit, layman-review, layman-compress

• 🔌 Works across major agents: Claude Code, Codex, Cursor, Windsurf, Gemini, Copilot, Cline

• 🆓 Free + MIT

Before and after

Normal agent output:
“Refactored validation pipeline, normalized response mapping, updated retry semantics, and aligned edge-case fixtures…”

Layman output:
“Fixed signup errors.
Users now get clear feedback.
Check invalid + valid signup once before release.”

Real note

Layman is strongest for coding-task handoffs.
For deep research or nuanced writing, longer output can still be better.
Token savings depend on mode and workflow — but the clarity gain is immediate.

Perfect for teams using AI daily who want less noise, fewer follow-up questions, and faster decisions.

Hey Product Hunt! I'm Don, maker of Octomind.

I've been building with AI coding agents for the past two years, and the same three problems kept killing my workflow:

Setup fatigue. Every new tool meant 45 minutes of configuring MCP servers, writing system prompts, and wiring dependencies before I could ask my first question.

Context rot. An hour into a session, the agent forgets the architecture decisions we made at the start. You end up repeating yourself constantly, or worse, the agent contradicts its own earlier reasoning.

Vendor lock-in. Hit a rate limit at 2am during a production incident? Too bad — your whole tool is welded to one provider.

Octomind is what I wanted to exist. It's a single Rust binary you install in 30 seconds. You pick a specialist agent from the tap registry (think Homebrew for AI) and you're working. No config files, no dependency hell, no MCP setup.

A few things I'm especially proud of:

- Tap system — 39 specialist agents across 10 categories (developer, devops, security, medical, legal, finance...). One command: `octomind run developer:rust`. Community can publish their own taps via Git.
- Adaptive compression — 72.5% token savings with zero quality loss. Your 4-hour session stays sharp because the runtime intelligently compresses history while preserving key decisions.
- 13+ providers, swap mid-session — Type `/model` and switch from Claude to GPT to DeepSeek to a local Ollama model. No restart, no lost context.
- Dynamic MCP — Agents extend themselves at runtime. They can register new tool servers mid-session without restarting.

The whole thing is Apache 2.0 open source. Every line of code is on GitHub. No telemetry, no cloud dependency, runs fully offline with Ollama.

I'd love to hear:
- What domains would you want a specialist agent for?
- If you've hit context rot or setup fatigue with other tools, what was your breaking point?

Happy to answer any questions about the architecture or the tap system. Let's talk!

Hey everyone! 👋 I'm James, the founder of Sharply.

I built Sharply because I was tired of seeing freelance directories that only cater to developers. There are millions of incredible freelancers out there — electricians, event planners, personal trainers, designers, writers, coaches — and they all deserve a clean, professional way to showcase their work.

Sharply gives every freelancer a public profile with their role, portfolio, skills, and platform links — all in one shareable URL. Think of it as a business card that actually proves you're legit.

A few things that make Sharply different:

Category-neutral — 14 guilds spanning every freelance discipline, not just tech
One link — your sharply.me/username pulls together everything a client needs to see
Sharp OG card — when you share your link on social media, it generates a custom preview card with your photo and role

I'd love to hear what you think. What features would make this more useful for your freelance work?

This is so cool, Rambo! I still remember as a kid I had a stuffed monkey named Wukong that I took to bed every night. I’d imagine all kinds of adventures with him, and even tried turning those stories into little hand-drawn books.

What you’ve built with Ethan Tales feels like bringing that exact childhood magic to life in a whole new way. Kids (and future parents like me) are going to love having a tool that turns their imagination into something real and lasting.

Hi, Michal.
Is your product also feasible for freelancers?