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Hyderabad
Joined January 2013
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Shashi Jakka । శశి జక్క retweeted
🚀 Meet Qwen-Image-2.1-Turbo — create and edit images in just 8 denoising steps! Open weights now available!
Built on Qwen-Image-2.1, Turbo is an accelerated checkpoint on the same 7B visual generation architecture.
Fewer steps does not mean lower quality: it still generates strong 2K images from text, and supports continued creation through natural-language edits, from adding accessories to changing a scene.
Start directly with Diffusers: load QwenImage21Pipeline and the checkpoint’s recommended 8-step sampling schedule is ready to go.
🌐 Prefer an API? Qwen-Image-2.1 Pro and Turbo APIs are now officially live. Choose a hosted API for your application, or use the Turbo weights in your own workflow.
🤖 ModelScope:
modelscope.cn/models/Qwen/Qw…
🤗 Hugging Face:
huggingface.co/Qwen/Qwen-Ima…
🔗 Pro API:
modelstudio.console.alibabac…
🔗 Turbo API:
modelstudio.console.alibabac…
What will you create with 8 steps? Share your results with us!
Meet Qwen-Image-2.1, the most balanced and cost-effective image generation model in the Qwen-Image series! Now open weights! 🎨
A unified model for both generation and editing, delivering top-tier quality in a lightweight package.
Highlights: 👀
- Compact & exceptionally fast: A lightweight 7B architecture that outperforms most closed-source models, with drastically accelerated inference for multi-image inputs.
- Native transparency: Natively generates and edits RGBA layers, enabling seamless compositing and text editing within transparent images.
- Versatile, high-fidelity editing: Supports up to 10 reference images and precise local control while preserving strict fidelity for portraits and products.
- Broad coverage & stunning aesthetics: Excels at panoramas, infographics, and virtual try-ons, delivering realistic textures and elegant typography.
Start to create your next masterpiece with Qwen-Image-2.1! 🖼️
- Blog: qwen.ai/blog?id=qwen-image-2…
- GitHub: github.com/QwenLM/Qwen-Image…
- Model Scope: modelscope.cn/models/Qwen/Qw…
- Hugging Face: huggingface.co/Qwen/Qwen-Ima…
Shashi Jakka । శశి జక్క retweeted
Tech companies trying to replace people with AI, only for people to start replacing tech companies with AI has gotta be the funniest plot twist of the century
Todos los productos de Adobe reimplementados desde cero, gratuitos y de código abierto
→ getartcraft.com/apps
ALT Interfaz de herramientas creativas para edición de imágenes, ilustración, video y desarrollo de bibliotecas.
Shashi Jakka । శశి జక్క retweeted
Someone on Reddit reverse-engineered their LG TV because the Plex app took 30 seconds to load
Built an entire native Plex client from scratch using Claude Code, Opus, and Fable.
Started in C, moved to Rust, ended up building his own OpenGL UI framework along the way
Same TV. Same hardware. Profile picker now loads in 3 seconds instead of 30. Runs at 60 fps. Supports 4K, Dolby Vision, Atmos. Fully open source
One person. One TV he was annoyed with. AI tools doing the work of an entire engineering team firmware reverse engineering, media playback, GPU profiling, shader optimization, UI architecture, all of it
Shashi Jakka । శశి జక్క retweeted
Build and test native Android apps seamlessly with @Antigravity.
By connecting the @StitchByGoogle MCP and the Android CLI, the agent imports UI designs, converts them into native Jetpack Compose components, validates everything in an emulator, and executes the final build directly on a physical device.
Get started with Antigravity today: goo.gle/4yCWX56
Shashi Jakka । శశి జక్క retweeted
Meet Brewlet! ☕ Introduced today at @Devoxx Belgium.
#Java on #Kubernetes, without bundling a JDK in every app image. Ship your JAR; manage JDKs on the nodes.
Learn more at brewlet.sh
Shashi Jakka । శశి జక్క retweeted
Simple example: fix a failing test.
messages = [prompt with code + error]
while True:
resp = model(messages, tools=[edit, run_tests])
if no tool calls: break
for call in resp.tools:
result = execute(call)
messages += [result]
That's the whole agent. LangChain wraps the same loop in AgentExecutor + chains + memory classes, adding layers that slow debugging.
Frameworks still useful for multi-provider apps, persistence, or heavy integrations. Core skill is the plain loop first.
Shashi Jakka । శశి జక్క retweeted
Learn the primitives instead: direct LLM API calls, tool/function calling, ReAct-style agent loops, context management, structured outputs, and solid evaluation. Build a few pure-Python agents from scratch first. The loop and patterns outlast any framework.
Shashi Jakka । శశి జక్క retweeted
AI can write the code.
AI can write the docs.
AI can write the tests.
AI can even write the PR description explaining why the code is definitely correct.
What it cannot reliably do is decide what should be built, ask the uncomfortable question in the room, convince people to change direction, or notice that everyone is solving the wrong problem.
As the cost of producing output keeps dropping, the value shifts to judgment, communication, taste, context, and knowing what not to do.
So yes, soft skills matter. They are just becoming increasingly hard requirements.
Soft skills are not really "soft" anymore.
Shashi Jakka । శశి జక్క retweeted
An unharnessed LLM can express intent, but it has no way to execute actions.
The harness is what connects model intent to the real world: reading files, running tests, and executing code.
Watch @BillyJacobson’s demo →
goo.gle/4AzfW1x
Shashi Jakka । శశి జక్క retweeted
The @antigravity team announced running local Gemma models on-device with LiteRT.
Good news for #Java developers: you can do this in Java too!
Antigravity Java SDK v0.2.17 is out, reaching full parity with Python SDK v0.1.18.
Walkthrough 🧵👇
glaforge.dev/posts/2026/09/2…
Shashi Jakka । శశి జక్క retweeted
Gemma 4 is now running locally on-device in the @Antigravity SDK. 🙌
Build fully local or hybrid multi-agent workflows that pair cloud models with a @GoogleGemma 4 workforce. Audit, patch, and test your code with total data privacy and zero API fees—all powered by LiteRT under the hood.
Shashi Jakka । శశి జక్క retweeted
Run open models like Gemma 4 completely offline in the Antigravity SDK.
Built on Google AI Edge’s LiteRT, you can now run Gemma 4 directly on your local GPU. Zero API costs, total data privacy, and no internet required.
Shashi Jakka । శశి జక్క retweeted
You can't one-shot an app. But most spec-driven setups are built for teams.
I wrote my own process down as a few skills and built a Spring Boot store with Stripe checkout. One PRODUCT.md. Specs that name their proof. 57 green tests.
youtu.be/bHJZiM2XM-w
Shashi Jakka । శశి జక్క retweeted
"Java is too slow for AI"... Nonsense.
The JVM can now transcribe speech up to 2X faster than the C++ reference implementation. Same model, same machine, same words.
An hour of audio in just 44 seconds.
No GPU. No Python. No ONNX. No Docker. No C++ sidecar.
github.com/qxoticai/qxotic/t…
Introducing the Google Cloud Developer Plugin for AI Coding Agents @googlecloud cloud.google.com/blog/topics…
Shashi Jakka । శశి జక్క retweeted
and now we have a spectrum - one end is deterministic scripts, the other end is reasoning models, and in between we have System One decision models like Jev.
Earlier, we split our skills/instructions into two pieces for efficiency, and now they would be split into three...
> deterministic, decisions, and reasoning
things that could be moved to decisions will go to system one modes; things that could be purely deterministic will go to traditional code, and the decision will sit with Jev.
new architectures and frameworks spawning up.