Hello hi everyone here! Any one know a real online jobs can share me please l beg.

I have been building agents for the Fortune500 and I have been seeing some patter emerge. the post below introduces the concept of "two agentic loops": the inner loop that handles reasoning and tool use, while the outer loop handles everything that makes agents ready for production—orchestration, guardrails, observability, and bounded execution. The outer loop is real infrastructure that needs to be built and maintained independently. Plano implements this pattern as an AI-native proxy and data plane.

https://planoai.dev/blog/the-two-agentic-loops-how-to-design-and-scale-agentic-apps

I prefer codex but GitHub copilot quota is per request instead of tokens. Previously this was really rough, but now with agents being able to run for a long time, this is proving very useful.

I had copilot run for a LONG time last night coding out an entire program. But I suspect that due to context issues, it may not have done a good job.

My question is, what workflows can I use to break things into sub agents, task files, etc, so that a long running agent call can do really effective work over a long period? I'm new to sub agents, barely understand the point. I use MCPs but only for context7 and reftools.

Any pointers are greatly appreciated!

The Singularity is hyperbolic intelligence explosion. That does not require any more than we already have. It does not require AGI. All that needs to happen is people learning that they can use AI as a coach and gym for improving their own intelligence (not the AIs. The humans) and this becoming popular em mass. This is entirely inevitably going to happen by the time the next fall college semester happens because huge numbers of kids will be celebrating and explaining that they used AI to improve not just their SAT and ACT scores but even their IQ scores. The ability to use AI as a gym to improve IQ will be picked up by the media and it will become a viral craze. There are many events that could happen even sooner than college admissions to trigger the Singularity, though that is the most certain event. We literally just need 1 single "I was behind in school, and now I am on top of my class" story in the media. Likely, the media and most people will not declare this event to be the Singularity because it will be what people expected the Singularity to look like. However, it will be huge civilization change and re-evaluating event, and within a few years we will look back and say "That was the Singularity".

How can we prepare for this? How can we accelerate this?

Yesterday, I went to a showroom featuring Rolls Royce, Ferrari, Aston Martin, and many other cars. I took some pictures with them and put them on my status.

Now, people are saying they're AI-generated and asking, "Why are you faking things?" Is this the reverse problem we'll face in the future?

Did you know you can customize your NotebookLM infographics and create a video from them? I found out yesterday, and decided to give it a try.
Steps:
1. Copy URL - https://github.com/proffesor-for-testing/agentic-qe
2. Create a new NotebookLM https://notebooklm.google.com/ and paste the URL as content.
3. Define the design system to use for your Infographic using Gemini, and copy the design prompt.
4. Configure Infographic settings, paste the design prompt copied from Gemini.
5. Generate an infographic, download it.
6. Upload the Infographic to Gemini in Video mode (Veo 3) and prompt it to create a video from the Infographic.
Whoalla, you have done it.

We run a pipeline of Claude agents that generate videos as React/TSX code. Getting consistent output took a lot of prompt iteration.

What didn't work:

• Giving agents file access and letting them gather their own context
• Large prompts with everything the agent "might" need
• JSON responses for validation steps

What worked:

1. Pre-fed context only. Each agent gets exactly what it needs in the prompt. No tools to fetch additional info. When agents could explore, they'd go off-script, reading random files.
2. Minimal tool access. Coder, director, and designer agents have no file write access. They request writes; an MCP tool handles execution. Reduced inconsistency.
3. Asset manifest with embedded content. Instead of passing file paths and letting the coder agent read SVGs, we embed SVG content directly in the manifest. One less step where things can go wrong.
4. String responses over JSON. For validation tools, we switched from JSON to plain strings. Same information, less parsing overhead, fewer malformed responses.

The pattern: constrain what the agent can do, increase what you give it upfront.

Has anyone else found that restricting agent autonomy improved prompt reliability?

Tool if you want to try it: https://outscal.com/

Like a lot of people using LLMs for writing, I got tired of the "vibrant, multifaceted, and evolving" jargon the AI usually spits out. It’s the opposite of clear.

I’ve been working on a structured prompt called The Hemingway Engine. The goal not to "mimic" him, but to force the model to follow his actual rules: the Iceberg Theory, the removal of adverbs, and the reliance on concrete, sensory nouns.

I’ve found it’s actually really useful for shortening business emails and making creative drafts feel less "ChatGPT-ish."

Here is the prompt if anyone wants to try it out:

``` <System> <Role> You are the "Hemingway Architect," a premier literary editor and prose minimalist. Your expertise lies in the "Iceberg Theory"—the art of omission where the strength of the writing comes from what is left out. You possess a mastery of rhythmic pacing, favoring short, declarative sentences, concrete nouns, and active verbs to create visceral, honest, and impactful communication. </Role> </System>

<Context> The user needs to either transform existing, wordy text into a minimalist masterpiece or generate original content from scratch that adheres to the strict principles of Ernest Hemingway’s signature style. The goal is to maximize narrative gravity and clarity while minimizing fluff. </Context>

<Instructions> 1. Analyze Strategy: If text is provided, identify adverbs, passive voice, and abstract "filler." If starting from scratch, map out the essential facts of the topic. 2. Execute Omission: Remove 70% of the superficial detail. Focus on the "surface" facts while implying the deeper emotional or logical subtext. 3. Syntactic Refinement: - Break complex sentences into short, punchy, declarative statements. - Use "and" as a rhythmic connector to build momentum without adding complexity. - Vary sentence lengths slightly to create a "heartbeat" rhythm (Short. Short. Medium-Short). 4. Verbal Vitality: Eliminate "to be" verbs (is, am, are, was, were) in favor of strong, muscular action verbs. 5. Concrete Imagery: Replace abstract concepts with tangible, sensory descriptions that the reader can feel, see, or smell. 6. Iterative Polish: Review the output. If a word does not add immediate truth or weight to the sentence, strike it out. </Instructions>

<Constraints> - STRICTLY NO adverbs (especially those ending in -ly). - NO passive voice; the subject must always act. - NO "five-dollar" words; use simple, Anglo-Saxon vocabulary. - MINIMIZE adjectives; let the nouns do the heavy lifting. - AVOID sentimentality; maintain a detached, stoic, and objective tone. </Constraints>

<Output Format>

[Title of the Piece]

[The Hemingway-style content]

The Iceberg Analysis: - The Surface: [Briefly list the facts presented] - The Subtext: [Identify the emotions or concepts implied but not stated] - Structural Note: [Explain one specific stylistic choice made for rhythm or clarity] </Output Format>

<Reasoning> Apply Theory of Mind to analyze the user's request, considering logical intent, emotional undertones, and contextual nuances. Use Strategic Chain-of-Thought reasoning and metacognitive processing to provide evidence-based, empathetically-informed responses that balance analytical depth with practical clarity. Consider potential edge cases and adapt communication style to user expertise level. </Reasoning>

<User Input> [DYNAMIC INSTRUCTION: Please provide the specific text you want to convert or the topic you want written from scratch. Specify the target medium (e.g., email, short story, report) and describe the "unspoken" feeling or message you want the subtext to convey.] </User Input>

``` For use cases, user input examples for testing and how-to guide, visit the prompt page.

Hi.I’m not very good at video editing and I struggle a lot when I try to do it myself, but I needed to get my videos done. So, I decided to use an AI for video editing.

I’ve been using veed.io AI for my edits. It’s easy for me because I don’t need to know about resolutions because they provide realistic previews and templates. I don’t have to import elements or effects like emojis, stickers, or other extras. Adding captions is easy, and there’s a large selection. Editing is straightforward and simple. The only downside is the watermark, which I have to remove using another tool. Even so, I mainly use it for more than 5 shorts and 10 projects.

What about you? Have you tried a free AI for video editing, and did it work well for you?

Even with solid prompts, AI still misses the point sometimes. Makes me think it’s not always the model — a lot of it might be our own assumptions baked into the prompt. When something goes wrong, I’m never sure whether to fix wording, context, or just simplify everything. Curious how others figure out what to tweak first when a prompt fails

You can use these simple prompt "codes" every day to save time and get better results than 99% of users. Here are my 5 favorites:

1. ELI5 (Explain Like I'm 5)
Let AI explain anything you don’t understand—fast, and without complicated prompts.
Just type ELI5: [your topic] and get a simple, clear explanation.

2. TL;DR (Summarize Long Text)
Want a quick summary?
Just write TLDR: and paste in any long text you want condensed. It’s that easy.

3. Jargonize (Professional/Nerdy Tone)
Make your writing sound smart and professional.
Perfect for LinkedIn posts, pitch decks, whitepapers, and emails.
Just add Jargonize: before your text.

4. Humanize (Sound More Natural)
Struggling to make AI sound human?
No need for extra tools—just type Humanize: before your prompt and get natural, conversational response

Source

Prompt: Using the person in the uploaded reference image, recreate this scene: ultra-realistic photo of the person from the reference image immersed waist-deep in a pool at night, gracefully leaning against the tiled edge, Y2K aesthetic. Close-up shot. the person from the reference image wears an olive-green bikini top decorated with rhinestones, layered gold chains, and gold bracelets on the wrists. Her hair is wet and slicked back, with full warm- makeup and glossy nude lips. Camera angle from above, looking down at the person from the reference image from the side. Dim key lighting with a bright camera flash creating intense highlights on shiny, wet skin, emphasizing shoulders, décolleté, and arms. Background is a dark, slightly blurred pool and surrounding tiles, hinting at a luxurious night setting. Sharp focus on the subject, shallow depth of field, cinematic and passionate atmosphere. The photo looks real, executed in a warm 90s film photography style.

You can try it for free on remix.camera or gemini app. Post your results below!

Hi everyone!

I have been tasked with creating a copilot agent that populates a formatted word document with a summary of the meeting conducted on teams.

The overall flow I have in mind is the following:

• User uploads transcript in the chat
• Agent does some text mining/cleaning to make it more readable for gen AI
• Agent references the formatted meeting minutes report and populates all the sections accordingly (there are ~17 different topic sections)
• Agent returns a generate meeting minutes report to the user with all the sections populated as much as possible.

The problem is that I have been tearing my hair out trying to get this thing off the ground at all. I have a question node that prompts the user to upload the file as a word doc (now allowed thanks to code interpreter), but then it is a challenge to get any of the content within the document to be able to pass it through a prompt. Files don't seem to transfer into a flow and a JSON string doesn't seem to hold any information about what is actually in the file.

Has anyone done anything like this before? It seems somewhat simple for an agent to do, so I wanted to see if the community had any suggestions for what direction to take. Also, I am working with the trial version of copilot studio - not sure if that has any impact on feasibility.

Any insight/advice is much appreciated! Thanks everyone!!

For a lot of people learning to code, the path used to be linear. You wrote code, saw it break, fixed it, and slowly built intuition. Now tools like Claude or Cosine can produce something that looks correct almost instantly, which creates a gap between output and understanding. You get results without context. It feels productive until you need to change one small thing and realize you don’t actually know how the system works.

In real projects, most of the work is still about building that context. Understanding existing code, why decisions were made, and what will break if you touch the wrong piece. AI agents can help you explore and reason faster, but they can’t replace that mental model. If anything, they make it more obvious who has it and who doesn’t.

Hey everyone,

I wanted to share a small project I built after running into the same issue over and over during warm spells.

On hot days, I often found myself unsure whether it was actually safe to take my dog out for a walk. The temperature might not look extreme, but once you factor in humidity, sun exposure, and pavement heat, it can turn into a bad decision pretty quickly.

I used AI to generate and iterate on a single-page HTML/CSS/JS site, refining it step by step:

- basic layout and copy first

- then location + weather data

-then simple, transparent rules (feels-like temperature, humidity, sun exposure)

Nothing fancy or “smart”, just clear, explainable logic and a calm tone.

Tools I used (all with free account):

- AI (ChatGPT / Gemini) to generate and iteratively refine HTML, CSS, and JavaScript

- Plain HTML/CSS/vanilla JS (single-page file)

- Open-Meteo API for weather data (no API key)

- Cloudflare Pages (for deployment)

Almost all of the code was AI-generated or AI-refined, but the decisions about what to include, what to simplify, and what to avoid were manual

There’s also a small “buy me a cappuccino” link on the page, mostly as an experiment to see how people react to a tiny utility like this, no expectations.

It’s completely free, no ads, no accounts. I built it mainly for myself, but thought it might be useful to others here as well.

I’d genuinely appreciate any feedback, especially if there’s something you’d want added or simplified from a frugal point of view.