Unlocking the agent future within organisations hinges on codifying their tribal knowledge into systematic apps that humans can easily operate and validate. At @QwikBuild, weโre already doing this on a large scale before deploying any agents.
Iโve compiled the patterns we used at @QwikBuild to manage context for large files in long-running AI agent threads at enterprise scale.
I hope itโs useful to other AI engineers working through the same problem. https://t.co/NuEX6YhCQn
A lot of enterprise AI pilots have quietly gone nowhere. The ones that worked started somewhere different.
We asked our founder Pradeep (@gunnygun3) for an approach that actually works. Here are 3 steps:
1. Find where the work actually lives.
2. Who you partner with matters more than people expect.
3. The FOMO has mostly settled.
Full answer in the video.
Voice comments and screenshot attachments are now live in Visual Feedback.
Speak what you want changed, show AgentQ what you're looking at, and hand it off to build ๐
Giving feedback to an AI agent should be as easy as explaining it to a human sitting next to you.
We just upgraded Visual Feedback in @QwikBuild ๐ข
You can now:
๐๏ธ Speak your feedback - add comments via voice and let AI soak in all the context. Powered by @SarvamAI Saaras v4 (great accuracy + speed).
๐ธ Attach screenshots - show AgentQ exactly what you're seeing instead of trying to explain everything in words.
Then hand it off to AgentQ to implement, or bring in a human expert through Dev Assist tickets.
(You probably won't need the latter. AgentQ is powerfullll.)
Sound on ๐ and watch this qwik demo.
Then bring us your most ambitious enterprise use cases at https://t.co/dk8eFdlag8
Both OpenAI and Anthropic have been chest thumping my model is greater by putting out claims that their models can find and exploit security vulnerabilities on their own.
The claim is exaggerated. What do you think?
#anthropic#openai#security
A large organisation is building a custom Applicant Tracking System with deep integration with Google Meet APIs. This will handle all their scheduling flows. Post interview analysis based on recordings from Google Meet. Iโm really enjoying how the product is shaping up. @QwikBuild
Big upgrade!๐
Visual Feedback is here โจ
No more paragraphs on โwhereโ things need to change, Just open the live preview, click the exact element, drop your comment, and let AgentQ see exactly what you mean.
Comment 'Feedback' for Qwikbuild access
Introducing Visual Feedback on @QwikBuild
The number one PAIN POINT across customers: giving change requests to AgentQ is harder than it should be.
Most users - developers, designers, and non-technical people alike - struggle to explain WHERE something is and WHAT needs to change. Not everyone thinks in terms like sticky headers, modals, hamburger menus, or layout containers.
So feedback turns into long descriptions, screenshots, and back-and-forth clarification:
โOn the page after I click A, where the text B appears under section C, change it to D.โ
That is too much friction. And it leaves customers exhausted.
Visual Feedback makes this natural.
Open the live preview next to your chat, click the exact area you want to change, and leave a comment. The agent gets the visual context, the element context, and your instruction.
All the complexity of describing the location and element gets consumed by one word: โthisโ
You can still toggle into โUse the appโ mode to navigate, sign in, and reach the right state. Once all comments are added, click โApply Comments,โ review them, and send the full context to the agent.
Less explaining. Less back-and-forth. Faster changes.
Welcome to the bright side.
Coding benchmarks like SWE-Bench and the latest one ProgramBench are useful, but can AI coding platforms like @qwikbuild, @replit and @lovable actually build and maintain real-world web applications?
Introducing SWE-WebDevBench: a comprehensive eval framework to assess AI coding platforms as virtual software development agencies, covering not just the middle step of coding, but the entire software lifecycle: Requirements gathering, planning, deployment and change management.
Preventify's founder built his MVP on Replit. Looked great. Broke in production.
What @QwikBuild actually did:
> Brainstormed requirements & architecture before writing code
> Built the platform with multi-agent coding harness
> Real developers fixed bugs & security issues on demand
> Now advising on AI agents & infra scaling
Live in production. Real patients. Topped up their annual plan.
The gap between demo and business is where we operate.
> Build now at qwikbuild. com/apply