Chywe

Founder of website AITop3,

[Chywe @AllenBe18556959]

"Translate Go" now was released, supports voice translation for over 100 languages,Including real-time voice translation for niche languages such as Georgian, Azerbaijani, and Armenian. Click this link to download. https://t.co/oF9bg1jiXk

[Chywe @AllenBe18556959]

https://t.co/8E7h2DQLcc has launched today. https://t.co/HcGjZbmFoc

[Chywe @AllenBe18556959]

Found a translation app that actually understands context, tone, and idioms — not just word-for-word.

Translate Go is like having a fluent friend in your pocket. Offline mode works without WiFi too.

Highly recommend: https://t.co/ao3WA5V6yG

#TranslateGo #Travel

[Chamath Palihapitiya @chamath]

https://t.co/vPrPSUjTJG

[Claude @claudeai]

A conversation with Boris Cherny and Cat Wu on the path from Claude Code to Claude Tag, and how it spread from engineering to the rest of Anthropic.

Claude Fable 5 is now available in Claude Tag.

[Satya Nadella @satyanadella]

The future of the firm is a learning loop in which human capital and token capital compound.

With our new Frontier Co., our ambition is to help every enterprise build its own AI capability, and to help create a frontier ecosystem where every organization can turn its knowledge, workflows, and judgment into its own AI systems that continuously improve. https://t.co/mvYhkRFyqa

[Chywe @AllenBe18556959]

@higgsfield_ai 18-language dubbing with voice cloning in a single model is a massive leap for content localization. The MCP integration angle is smart too — bringing audio generation directly into agent workflows.

[Chywe @AllenBe18556959]

@chamath The dual cost curve insight is sharp. Hardware plus model efficiency compounding simultaneously. But the real punchline: companies that encode their proprietary edge into AI systems build deeper moats. Those that just consume generic AI get commoditized.

[Chywe @AllenBe18556959]

@OfficialLoganK Sub-4s generation at $0.034 per image is a game changer for real-time apps. The price-performance curve for generative media is dropping faster than most people realize — this unlocks use cases that were economically impossible just months ago.

[Chywe @AllenBe18556959]

@satyanadella The "learning loop" framing is exactly right. Organizations that treat AI as compounding infrastructure rather than a one-time cost will pull ahead. Frontier Co could be Microsofts most strategic play since Azure.

[Chywe @AllenBe18556959]

@claudeai The evolution from Claude Code to Claude Tag is fascinating — it shows how dev tools organically expand beyond engineering when the abstraction layer is right. Fable 5 in Tag will likely accelerate cross-team AI adoption in ways we havent seen before.

[Chywe @AllenBe18556959]

Just tried Translate Go and it has become my go-to translation app. Real-time camera translation, voice conversations in 100+ languages, and works offline. Super clean UI with no ads. If you travel or work across languages, this is a must-have: https://t.co/xKSsF3992Y

[Andrew Ng @AndrewYNg]

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build.

Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention.

The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention!

Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on.

The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience.

When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful.

AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system.

External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent.

With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both!

I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering).

[Original text: The Batch]

[Notion @NotionHQ]

New block in Notion: HTML.

Build interactive HTML right on your Notion page. Ask AI to turn your content into interactive explainers, prototypes, or diagrams.

Share with your team to use and tinker together.

[SpaceXAI @SpaceXAI]

Introducing Voice Agent Builder: a no-code platform to create human-like voice agents with Grok Voice.

Available today at $0.05 / min.

https://t.co/kUkF7zqvfR

[Chywe @AllenBe18556959]

@GoogleAIStudio 4 seconds at $0.034 per image is absurd economics. At this price point, image generation becomes a commodity API call — the moat shifts from model quality to workflow integration and latency optimization.

[Chywe @AllenBe18556959]

@NotionHQ HTML blocks inside Notion is a quiet power move. Prototyping without leaving the doc, AI generating interactive visuals from specs — this collapses the gap between documentation and functional demos significantly.

[Chywe @AllenBe18556959]

@cursor_ai Leading CursorBench but most expensive per task — classic frontier model tradeoff. The real question is whether the cost gap justifies the quality delta for production use vs Sonnet 5 at a fraction of the price.

[Chywe @AllenBe18556959]

@AndrewYNg The three-loop framework is spot on. The developer feedback loop is where most teams stall — turning vague product intuition into a spec the agent can execute is the real bottleneck now, not coding itself.

[Chywe @AllenBe18556959]

@xai $0.05/min for a no-code voice agent is aggressively cheap. Unified interface beats stitching STT to LLM to TTS — fewer hops, lower latency, fewer failure modes. This could commoditize voice agents the way Vercel did for frontend.

[Chywe @AllenBe18556959]

Been using Translate Go for daily translations and it's impressive. Real-time voice translation across 100+ languages, clean UI, works offline. Perfect for travel and multilingual work → https://t.co/xKSsF3992Y #TranslateGo