sathish316

7 ms·

61.
ShowHN: Make OpenClaw respond in Scarlett Johansson’s AI Voice from the Film Her(twitter.com)
1 points by sathish316 8mo ago|4 comments
62.
by sathish316 8mo ago
AI commoditized to the point where Nash Equilibrium and 35 minutes between announcements matter :)
63.
by sathish316 8mo ago
OpenClaw does present security risks, and the recommendations outlined in this article are apt. That said, OpenClaw is more powerful than Claude Code due to its self-evolving agent architecture and its unfettered access to terminal and tool
64.
OpenClaw – Secure Storage of Password and API Keys Using HashiCorp Vault(twitter.com)
1 points by sathish316 8mo ago|1 comments
65.
by sathish316 8mo ago
For OpenClaw/Clawdbot users: Do you want to securely store passwords/API keys in OpenClaw instance, and gradually unlock more AI skills or superpowers from your agent? This step‑by‑step guide shows how to add a secure Vault layer
66.
by sathish316 8mo ago
You can run an ensemble of LLMs (Opus, Gemini, Codex) in Claude Code Router via OpenRouter or any Agent CLI that supports Subagents and not tied to a single LLM like Opencode. I have an example of this in Pied-Piper, a subagent orchestrator
67.
by sathish316 8mo ago
I do think there’s a real risk of Brain Atrophy when you rely on AI coding tools for everything and while learning something new. About a year ago, I dealt with this problem by using Neovim and having shortcuts like below to easily toggle G
68.
by sathish316 8mo ago
Some native Claude code options when your quota runs out: 1. Switch to extra usage, which can be increased on the Claude usage page: https://claude.ai/settings/usage 2. Logout and Switch to API tokens (using the ANTHRO
69.
by sathish316 8mo ago
Claude Code Router or ccr can connect to OpenRouter. When your quota runs out, it’s a much better speed vs quality vs cost tradeoff compared to running Qwen3 locally - https://github.com/musistudio/claude-code-router
70.
by sathish316 9mo ago
+1 I’m not sure if tasks like Add OTel instrumentation belongs more in a Coding bench than an SRE bench. I came here expecting to see things like, this is how Models perform on finding the root cause in 50 complicated microservice failure s
71.
by sathish316 9mo ago
Subagent orchestration without the overhead of frameworks like Gastown is genuinely exciting to see. I’ve recorded several long-running demos of Pied-Piper, which is a Subagents orchestration system for Claude Code and ClaudeCodeRouter+Open
72.
by sathish316 9mo ago
Pied-Piper is another Subagents orchestration system that works from a single Claude code session with an orchestrator and multiple agents that handoff tasks to each other to follow a workflow - https://github.com/sathish316
73.
by sathish316 9mo ago
This is the best summary of everything that’s wrong with: 1. One-shot app development using Lovable with English requirements 2. Spec driven development tools like SpecKit, Kiro that promise English as being sufficient for LLM generated cod
74.
by sathish316 9mo ago
Excellent article on the internals of coding CLIs. I learned a similarly powerful way to build DIY coding CLIs from this Martin Fowler post, which uses PydanticAI and MCP-based tools: https://martinfowler.com/articles/b
75.
by sathish316 9mo ago
Complex planning and orchestration for Multi-step usecases or persistent Todo lists is achievable by spinning up your own tools that does something similar to this. By extending Claude Todo emulator, It was possible to make the agent come u
76.
by sathish316 9mo ago
It’s surprising how simple TodoWrite and TodoRead tools are in planning and making sure an Agent follows the plan. This is supposed to be an emulator of Claude’s own TodoWrite and TodoRead, which does a full update of a todo.json for every
77.
by sathish316 9mo ago
I had the same thought of using beads to build a multi-agent orchestrator with a defined set of workflows. But to keep things tractable, i've kept the orchestration within a collection of subagents in a single Claude code session. The
78.
by sathish316 10mo ago
It’s not for building 10 complex or overlapping features at a time. Parallel agents are useful for: 1. Offloading minor refactoring work 2. Offloading non-overlapping features 3. Offloading unit or integration test additions 4. Exploring mu
79.
by sathish316 10mo ago
Could you share this script as a gist? https://gist.github.com
80.
by sathish316 10mo ago
Nice work! You might also want to look at Vibe Kanban, which supports similar features across multiple projects and multiple coding CLIs - https://www.vibekanban.com/ Superset will be a good alternative for someone who is u
81.
by sathish316 10mo ago
Can you do @ and refer to a method or variable in a file with lsp support? Otherwise, how can lsp context be used in Terminal chat?
82.
by sathish316 10mo ago
Pied-Piper is an OSS tool to Create a Team of AI Coding Subagents to work on long-running/complex SDLC workflows. The Subagents can run on Claude Code or any Coding CLI that supports Subagents and are fully customizable without changin
83.
Pied-Piper: Create Team of AI Coding Agents for Long-Running/Complex SDLC Tasks(github.com)
2 points by sathish316 10mo ago|1 comments
84.
by sathish316 11mo ago
The framework has the following capabilities: 1. A way to create function tools 2. A way to create specialised subagents that can use their own tool or their own model. The main agent can delegate to subagent exposed as a tool. Subagents do
85.
by sathish316 11mo ago
I agree with your analysis of building your own Agent framework to have some level of control and fewer abstractions. Agents at their core are about programmatically talking to an LLM and performing these basic operations: 1. Structured Inp
86.
by sathish316 1y ago
This YouTube video by the Raft creators (linked at the bottom of the page) gives a solid explanation of Raft internals - https://m.youtube.com/watch?v=YbZ3zDzDnrw
87.
by sathish316 1y ago
Isn’t this the basis of GAN (Generative Adversarial networks), which is how most GenAI image models work? The purpose of generator network is to generate data that is as close to the training set as possible. The purpose of discriminator ne
88.
by sathish316 1y ago
Leetcode me if you can
89.
by sathish316 3y ago
Apple M1 mac GPU is orders of magnitude slower than a single Nvidia T4 GPU with CUDA for certain machine learning tasks. This was my experience while trying to run Stable diffusion Inpainting/Outpainting to expand images. This task too
90.
by sathish316 4y ago
“The long-term solution to ATT, though, is to build probabilistic models that not only figure out who should be targeted, but also understanding which ads converted and which didn’t.” What does this even mean? Is it really possible to build

More ›