Harmanjot Singh

Ancient God(self-diagnosed)

[Harmanjot Singh @senghsahab]

Unlearning your own toxic patterns as a form of self care -🅿️

[Harmanjot Singh @senghsahab]

@brittnaynay3 @msigaming Man I really do need a pC

[Nay.Tesseract @brittnaynay3]

One of you is taking home this $12K MSI MEG PC 👀🔥🖤

How to enter:

1️⃣ Like this post
2️⃣ Follow @brittnaynay3 @msigaming
3️⃣ Comment + repost 🔁

That’s it! ✅

🌎 International where legally permitted. Full Terms & Conditions + extra entry opportunities are linked below in comment ⬇️

Good luck 🖤🔥

[Harmanjot Singh @senghsahab]

@merishabh_singh @makemytrip Interested

[Harmanjot Singh @senghsahab]

@VittoStack AI

[Harmanjot Singh @senghsahab]

@Rajesh992510253 @UpSkillYourLife Yt

[Harmanjot Singh @senghsahab]

@asmah2107 Hello there!!

[Harmanjot Singh @senghsahab]

@mikefutia REELS

[Harmanjot Singh @senghsahab]

@nitesh_singh5 I'm Interested

[elvis @omarsar0]

As usual, Anthropic just published another banger.

This one is on context engineering.

Great section on how it is different from prompt engineering.

A must-read for AI devs.

[Carlos E. Perez @IntuitMachine]

How is it possible that Claude Sonnet 4.5 is able to work for 30 hours to build an app like Slack?! The system prompts have been leaked and Sonnet 4.5's reveals its secret sauce!

Here’s how the prompt enables Sonnet 4.5 to autonomously grind out something Slack/Teams-like—i.e., thousands of lines of code over many hours—without falling apart:

It forces “big code” into durable artifacts. Anything over ~20 lines (or 1500 chars) is required to be emitted as an artifact, and only one artifact per response. That gives the model a persistent, append-only surface to build large apps module-by-module without truncation.

It specifies an iterative “update vs. rewrite” workflow. The model is told exactly when to apply update (small diffs, ≤20 lines/≤5 locations, up to 4 times) versus rewrite (structural change). That lets it evolve a large codebase safely across many cycles—how you get to 11k lines without losing state.

It enforces runtime constraints for long-running UI code. The prompt bans localStorage/sessionStorage, requires in-memory state, and blocks HTML forms in React iframes. That keeps generated chat UIs stable in the sandbox while the model iterates for hours.

It nails the dependency & packaging surface. The environment whitelists artifact types and import rules (single-file HTML, React component artifacts, CDNs), so the model can scaffold full features (auth panes, channels list, message composer) without fighting toolchain drift.

It provides a research cadence for “product-scale” tasks. The prompt defines a Research mode (≥5 up to ~20 tool calls) with an explicit planning → research loop → answer construction recipe, which supports the many information lookups a Slack-like build needs (protocol choices, UI patterns, presence models).

It governs tool use instead of guessing. The “Tool Use Governance” pattern tells the model to investigate with tools rather than assume, reducing dead-ends when selecting frameworks, storage schemas, or deployment options mid-build.

It separates “think” and “do” with mode switching. The Deliberation–Action Split prevents half-baked code sprees: plan (deliberation), then execute (action), user-directed. Over long sessions, this avoids trashing large artifacts and keeps scope disciplined.

It supports long-horizon autonomy via planning/feedback loops. The prompt’s pattern library cites architectures like Voyager (state + tools → propose code → execute → learn) and Generative Agents (memory → reflect → plan). Those loops explain how an LLM can sustain progress across dozens of hours.

It insists on full conversational state in every call. For stateful apps, it requires sending complete history/state each time. That’s crucial for a chat app where UI state, presence, and message history must remain coherent across many generation cycles.

It bakes in error rituals and guardrails. The pattern language’s “Error Ritual” and “Ghost Context Removal” encourage cleaning stale context and retrying with distilled lessons—vital when a big build hits integration errors at hour 12.

It chooses familiar, well-documented stacks. The guidance warns about the “knowledge horizon” and recommends mainstream frameworks (React, Flask, REST) and clean layering (UI vs. API). That drastically improves throughput and correctness for a Slack-like system.

It enables “Claude-in-Claude” style self-orchestration. The artifacts are allowed to call an LLM API from within the running artifact (with fetch), so the model can generate a dev tool that helps itself (e.g., codegen assistant, schema migrator) during the build.

It keeps outputs machine-parseable when needed. Strict JSON-only modes (and examples) let downstream scripts/tests wrap the app and auto-verify modules, enabling unattended iteration over many hours.

Put together, these prompts/patterns create the conditions for scale: a safe sandbox to emit large artifacts, iterative control over code evolution, disciplined research and tool usage, long-horizon memory/plan loops, and pragmatic tech choices. That’s how an LLM can realistically accrete ~10k+ lines for a Slack-style app over a long session without collapsing under its own complexity.

[Harmanjot Singh @senghsahab]

@durgesh_n8 Ola le bhai .

[Akhilesh Mishra @livingdevops]

Early in my DevOps career, I deleted a 5GB log file from a production server that was running out of space.

I ran df -h expecting to see the disk usage drop. It didn’t.
Still showed 100% full.

No errors, no warnings. Just the same disk usage as before I deleted anything.

That’s when I learned that deleting a file doesn’t always free up space immediately.

In Linux, what we think of as a “file” is actually two separate things: the filename (which is just a pointer) and the inode (which contains the actual data and metadata). When you delete a filename, you’re only removing the pointer. The inode and its data remain on disk as long as any process still has the file open.

In my case, the web server was still writing to that log file. Even though I had deleted the filename, the server process kept its file handle open. The inode stayed alive, invisible to normal file listings but still consuming disk space.

The space was only freed when I restarted the web server, which closed all its file handles.

This is why you need different commands to see the full picture:

# Check filesystem usage
- df -h

# Check actual directory sizes
- du -sh /var/log/*

# Find deleted files still open by processes
- lsof +L1

The du command shows you what’s actually using space in directories, while df shows filesystem-level usage.

When they don’t match, you often have deleted files still held open by running processes.

This is also why proper log rotation doesn’t just delete files. Tools like logrotate rename files and send signals to processes so they can close and reopen their file handles cleanly.

Three key takeaways:

1. Filenames are just pointers to inodes
1. Deletion only happens when no processes reference the inode
1. Always check both df and du when troubleshooting disk space

It’s a small detail, but understanding it can save you from confusing production incidents.

[yatri doctor @DoctorYatri]

Last Night Me and my friend was Arrested by police, Handcuffed and taken to Police Station in Burkina Faso, Everything was confiscated after 4 hours we got our phone for few minutes and instead of calling my parents I called Indian embassy in BF on Emergency Line

[Navdeep singh @ofcnavdeepbrar]

Asi dono hi kal burkina faso pohche c sanu pohchde hi road to police ne Arrest kr leya te asi kayi war indian embassy nu call kiti par koi jawaab nai mileya te na hi kise ne whatsapp te msg dekhe even net off krke baithe c

[Harmanjot Singh @senghsahab]

@mpanktiya Harmanjot Singh

[Harmanjot Singh @senghsahab]

@NVIDIAGeForce #BlackMythRTX good luck!!

[Harmanjot Singh @senghsahab]

@cognition_labs Bro I didn't ven finish my degree yet 😭

[Global Government Affairs @GlobalAffairs]

The Indian government has issued executive orders requiring X to act on specific accounts and posts, subject to potential penalties including significant fines and imprisonment. 

In compliance with the orders, we will withhold these accounts and posts in India alone; however, we disagree with these actions and maintain that freedom of expression should extend to these posts.

Consistent with our position, a writ appeal challenging the Indian government's blocking orders remains pending. We have also provided the impacted users with notice of these actions in accordance with our policies.

Due to legal restrictions, we are unable to publish the executive orders, but we believe that making them public is essential for transparency. This lack of disclosure can lead to a lack of accountability and arbitrary decision-making.

[Anime Shots @anime_shots]

Today October 31st, Gojo Satoru was sealed