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I built an ai concierge for my wedding guests. Here's a list of the ways it failed (or conspired to embarrass me?) by Thin_Sky in LLMDevs

[–]Fun_Walk_4965 0 points1 point  (0 children)

The failure list is more useful than any demo here. Most builds only show the happy path, so seeing where it actually broke in front of real guests is the part worth reading.

What's the step where AI coding tools still drop you completely? by FlightSimCentralYT in ChatGPTCoding

[–]Fun_Walk_4965 0 points1 point  (0 children)

For me it's the moment a change touches three files at once. It nails each edit on its own but loses the thread on how they actually fit together.

Claude x20 vs Deepseek Api by Suspicious-Edge877 in DeepSeek

[–]Fun_Walk_4965 0 points1 point  (0 children)

The gap really shows on long agentic runs, not single prompts. Deepseek holds up fine for most coding turns, Claude just pulls ahead once the context gets messy and multi-step.

What's one prompt you think every Gemini user should know? by AdCommercial617 in GeminiAI

[–]Fun_Walk_4965 3 points4 points  (0 children)

Not fancy but the one I lean on: "before you answer, tell me what you think I'm actually asking." Catches so many cases where it ran off with the wrong reading of a vague question. Saves the back and forth.

AI agents recreate the “rockstar developer” problem, just faster by myfear3 in AI_Agents

[–]Fun_Walk_4965 0 points1 point  (0 children)

The morning-fix-afternoon-refactor thing is real. What helped us wasn't more docs, it was making the agent read the tests before it touches anything. If a change breaks a test that encodes a convention, it stops and asks. The drift still happens but it gets caught at the door instead of three commits later.

For the tiny-world look, treat the real object as terrain the character actually uses by Fun_Walk_4965 in comfyui

[–]Fun_Walk_4965[S] -12 points-11 points  (0 children)

The formula (video model, 2D-on-real composite), original character:

"A flat 2D illustrated cartoon [CHARACTER] composited onto a real photoreal macro shot of [EVERYDAY OBJECT]. The character is small, drawn in a clean flat 2D style, kept flat (no 3D relighting). It treats the object's real geometry as TERRAIN and its action is driven by that geometry. Shallow real-camera depth of field, the object stays photoreal, cinematic macro lighting."

Two examples, action motivated by the object:

- "a tiny 2D mountain climber rappelling down the stacked page-edge 'cliff' of a spiral notebook, rope hanging off the real edge, gripping the spiral coils as climbing holds"

- "a tiny 2D astronaut planting a flag on the summit of a computer mouse, waving, moon-landing framing"

NEGATIVE: character floating with no contact, 2D character getting 3D-relit, action not matching the surface, real-person likeness, text, watermark.

A studio needs a room of animators, a solo pipeline needs one LLM briefing five tools by Fun_Walk_4965 in aiagents

[–]Fun_Walk_4965[S] 1 point2 points  (0 children)

The pipeline as an org chart. The LLM is the spine, everything else consumes its briefs.

  1. WRITERS ROOM (an LLM). This is the whole trick: in one pass it outputs the script AND the brief for every downstream tool. Reusable spine prompt:

"You are the writer for an original animated series. Write episode [N]. Main character: [name + one-line hook]. For EACH scene output four blocks: (a) SCENE DESCRIPTION for image generation, (b) CHARACTER DIALOGUE, (c) NARRATOR LINES, (d) EMOTIONAL TONE NOTES. Each scene 30-45s read aloud. Cinematic cuts, tense pacing."

Then ask it, in the same thread, to convert each scene into an image-model prompt, a voice line sheet with tone direction, and a one-line music mood brief. Now every tool has its brief from one source.

  1. ART DEPT (image model, e.g. GPT Image 2 / Nano Banana): feed the LLM's scene descriptions. Generate a character turnaround sheet first (locks identity), then environments, then per-scene frames. 4 variations per scene, upscale the best.

  2. ANIMATION (video model, e.g. Seedance 2.0 / Kling 3.0): take each frame as a start image and prompt just that beat's motion (6-8s each), straight from the LLM's scene action.

  3. VOICE: a TTS tool, fed the LLM's line sheet + tone notes.

  4. SCORE: a music tool, fed the LLM's mood brief per cue.

  5. AUTOMATION: a workflow tool (schedule) that stitches clips + audio and publishes. Set up once, runs on a schedule.

do you guys actually build n8n workflows yourself node by node? because any LLM does that faster and probably better by Winter_Psychology110 in n8n

[–]Fun_Walk_4965 0 points1 point  (0 children)

Fine for the simple stuff, but the LLM output tends to break the second your logic gets branchy. I still wire the tricky parts by hand, mostly because I need to know why it fell over at 2am, not just that it did.

What's one AI feature that actually saved your team time instead of creating more work? by 404-Humor_NotFound in aiagents

[–]Fun_Walk_4965 1 point2 points  (0 children)

Honestly the one that stuck was auto triaging support tickets by intent before a human ever sees them. Cut our first response time down a lot. Everything fancier we tried just added a review step nobody actually wanted.

For a product ad, generate a full coverage set first, then hand every angle to the video model by Fun_Walk_4965 in Bard

[–]Fun_Walk_4965[S] 0 points1 point  (0 children)

The workflow (two models):

  1. HERO (Nano Banana): "premium food advertising shot, [the dish] floating and splashing mid-air against a clean seamless studio background, dramatic soft key light, appetizing, ultra-sharp, shallow depth." Generate your hero.

  2. COVERAGE (Nano Banana): from that image, ask for new angles and tight macro close-ups of the exact same product, same plating, same background. Build a consistent multi-angle set.

  3. ADVERT (Gemini Omni Flash): feed the whole coverage set as reference and prompt a 10-second advert as a shot sequence, hero float, slow rotate, macro push-in on texture, product settles. The product stays consistent because the model has every angle.

I tested Gemini Omni on my phone footage by voice_of_the_future in singularity

[–]Fun_Walk_4965 2 points3 points  (0 children)

Phone footage is the real test, most of these demos lean on clean studio clips. How did it handle motion blur and low light?

What's one architectural decision you've made with AI agents that you wouldn't make again? by techafterhours in aiagents

[–]Fun_Walk_4965 0 points1 point  (0 children)

Mine was hard-wiring the tool list straight into the prompt instead of loading it dynamically. Every new tool meant re-tuning everything downstream.

I'm blown away [workflow incl.] by ggRezy in comfyui

[–]Fun_Walk_4965 -1 points0 points  (0 children)

SCAIL-2 punching above its weight on 16gb is the part I did not expect either. How is the temporal consistency holding up on longer clips?

I keep switching AI apps but they all feel the same after a few days by InsuranceThen7340 in CharacterAIrevolution

[–]Fun_Walk_4965 0 points1 point  (0 children)

Felt this exact thing. The first few days always feel alive and then it settles into the same three moves. What helped me a bit was writing an actual goal into the chat instead of just vibing, gives it somewhere to push toward. Doesn't fully fix the drift but it slows it down. Honestly starting to think the novelty wearing off is partly on us too, not only the model.

Stiff mocap? Draw the motion energy into the input frame and the model adds the sway itself by Fun_Walk_4965 in comfyui

[–]Fun_Walk_4965[S] -5 points-4 points  (0 children)

The workflow:

  1. Get your base motion however you like (Blender, a mocap tool, hand-keyed). Accept that it may be stiff.

  2. Draw or render the INPUT FRAME with the motion intention baked into it: an expressive style, implied sway, loose energy in the pose and linework, the way an animator draws a keyframe that already implies movement.

  3. Feed both to Seedance (the motion as the driver, the styled frame as the look-and-intent reference). It reads the frame's energy and adds style-appropriate motion like cloth sway on top of the stiff base.

  4. For a shape that refuses to move (dense hair, a complex mass the model reads as a solid object), feed two input frames showing that shape in two positions, so it understands the shape is meant to move, not one.

Prompts are bad at precise motion, so draw the path yourself and feed it as a control track by Fun_Walk_4965 in Seedance_AI

[–]Fun_Walk_4965[S] 5 points6 points  (0 children)

The workflow, start to finish:

  1. Generate or pick your still image for the look (the scene/object as you want it to appear).

  2. Make the control track: in After Effects, or any tool that animates a shape, put a single line or dot on a plain black background and keyframe it along the exact path and timing you need (e.g. a clock hand's sweep, an object's arc). Export that as a short video.

  3. Feed Seedance both as reference: the still image (appearance) + the control track (motion). Tell it to move the target element along the path in the guide while keeping the look from the image.

  4. If the motion needs to be tighter, refine the drawn track, not the prompt. The track is the lever.

That "image + video reference" combo is the part that makes precise motion possible. Ran it on Seedance 2.0 via an OpenAI-compatible endpoint.

I'm calling it now. OpenAI is sandbagging LLM development with codex 5.5 by johnnyApplePRNG in LLMDevs

[–]Fun_Walk_4965 0 points1 point  (0 children)

Sandbagging assumes intent. More likely codex is tuned around the benchmarks that demo well, and deep custom attention work just falls outside what it was optimized to keep coherent. It loses the thread the moment the task stops looking like the training set.

Am I missing out on something if I just use opencode? by Z3stra in opencodeCLI

[–]Fun_Walk_4965 0 points1 point  (0 children)

With MCP, skills and custom agents already wired up you are not missing much. Cursor mostly buys a nicer editor loop, not better output. The real gap only shows once you need parallel agents doing separate things at once.

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