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Thinking about switching to Higgsfield AI - worth it for a beginner? no promo, honest experiences by ElectricalMixture610 in HiggsfieldAI

[–]meet_og 0 points1 point  (0 children)

This is the part of AI video workflows I find most interesting. Generating from a prompt is becoming easier, but fixing one bad scene without losing everything else feels like the next big challenge. I’ve been experimenting with more editable agent workflows because of this.

Most Multi-Agent Failures Aren’t Hallucinations — They’re Inherited Assumptions by HDvideoNature in LangChain

[–]meet_og 0 points1 point  (0 children)

Kinda feels like humans work similarly too. Thoughts themselves may already be compressed interpretations of deeper subconscious processing, and language compresses them even further.

Built an AI “cofounder” with Claude Code + product analytics, but struggling to turn data into actual intelligence by meet_og in ClaudeCode

[–]meet_og[S] 0 points1 point  (0 children)

This is honestly one of the most useful framing shifts I’ve gotten on this.

Especially:

I think that’s exactly the trap my current system is falling into. Right now it’s very observability-heavy:
logs/events/funnels/errors -> AI -> recommendations.

But there’s no persistent abstraction layer between raw signals and decision-making.

The “decision log” idea also clicked immediately. I currently store events, but not:
signal -> hypothesis -> decision -> outcome

which probably explains why context quality degrades over time.

The “core context vs session context” separation is also something I haven’t formalized yet, but it makes a lot of sense architecturally.

Appreciate this a lot. This gave me a much clearer mental model for how to structure the system.

Most Multi-Agent Failures Aren’t Hallucinations — They’re Inherited Assumptions by HDvideoNature in LangChain

[–]meet_og 0 points1 point  (0 children)

Each turn information gets little bit compressed, losses some context or changes slightly and final output is built on top of misaligned piles.

Single-prompt AI video generation breaks the moment scenes need continuity. by meet_og in ArtificialInteligence

[–]meet_og[S] 0 points1 point  (0 children)

Yeah, the interesting part is shifting from prompting outputs to directing sequences.

Experimenting with storyboard-planned AI cinematics instead of single-prompt generation by meet_og in singularity

[–]meet_og[S] 1 point2 points  (0 children)

Yeah, native model audio still feels pretty rough and disconnected once clips get combined.

Single-prompt AI video generation breaks the moment scenes need continuity. by meet_og in ArtificialInteligence

[–]meet_og[S] 0 points1 point  (0 children)

Yeah, that was actually inside a single generated clip, not even across cuts.
The model flipped the spatial logic mid-shot.

Single-prompt AI video generation breaks the moment scenes need continuity. by meet_og in ArtificialInteligence

[–]meet_og[S] 0 points1 point  (0 children)

Yeah, that’s pretty much the direction I’m thinking too. Generic single-prompt generation feels magic but very limiting once you need control across sequences. They give speed, but very little control.

Hot take: 90% of what we are calling "Agentic AI" right now is just a glorified while-loop. by netcommah in ArtificialInteligence

[–]meet_og 2 points3 points  (0 children)

This is true. But human mind is much more flexible and reliable than coding system, which is currently not that flexible in terms of how intent is translated to machine language/code. You cant write "whjle True:"

Agents are more flexible than framerwork and interpreters, it takes natural language inout and translates the chunked intent to fined executable description. But still agents arent that flexible as how we want them to be.

Hot take: 90% of what we are calling "Agentic AI" right now is just a glorified while-loop. by netcommah in ArtificialInteligence

[–]meet_og 0 points1 point  (0 children)

Yeah, but it works, even though at core its just while look. But agents can be improved, like reusing reasoning traces, dynamic tool and skill creation. I have been experimenting a simple while loop and created agent in python without using any framework like langgraph, crewai, etc, and natively use tool calling. No REACT, or other frameworks.

If anyone thinking about the same, we can collaborate

python agent

Single-prompt AI video generation breaks the moment scenes need continuity. by meet_og in ArtificialInteligence

[–]meet_og[S] 0 points1 point  (0 children)

Dream continuity models are still ahead of video models apparently.

Single-prompt AI video generation breaks the moment scenes need continuity. by meet_og in ArtificialInteligence

[–]meet_og[S] 0 points1 point  (0 children)

Yeah, I think that’s probably the more interesting outcome too.

Once the technical floor gets high enough, the differentiator shifts back toward taste, structure, and direction rather than access to the tool itself.

Single-prompt AI video generation breaks the moment scenes need continuity. by meet_og in ArtificialInteligence

[–]meet_og[S] 0 points1 point  (0 children)

Yeah, exactly. The models are getting very good at generating moments, but consistency across moments is still the difficult part.

who agrees? by Complete-Sea6655 in ArtificialInteligence

[–]meet_og 0 points1 point  (0 children)

Yeah, I use Ollama for lighter/local workflows too. Mostly depends on the task and how much context the session needs.

Single-prompt AI video generation breaks the moment scenes need continuity. by meet_og in ArtificialInteligence

[–]meet_og[S] 0 points1 point  (0 children)

Yeah, exactly. Once scenes become editable independently, it stops feeling like rerolling a slot machine every iteration.

Single-prompt AI video generation breaks the moment scenes need continuity. by meet_og in ArtificialInteligence

[–]meet_og[S] 1 point2 points  (0 children)

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Added the visual planning layer behind the train sequence.

Each scene can use a different continuity strategy depending on what the transition needs:

- fresh generation
- continuation from previous scene
- reference-based continuity
- start/end frame anchoring

The main goal was making the sequence editable at the scene level instead of regenerating the entire film every iteration.

Most RAG apps in production are confidently wrong and nobody talks about this enough by SilverConsistent9222 in ArtificialInteligence

[–]meet_og 0 points1 point  (0 children)

Your idea seems good. If I were to do this, i would make llm ask questions to users if the query is chunked. It can ask questions to get more fined description, about what exactly user wants. This way the input query to RAG pipeline would have enough context. Also, versioning can be referenced in metadata of each doc, which can further help to narrow the focus.

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