u/Jorvex609

Are there any uncensored models? One of the issues I ran into with llama is that any discussion that sits adjacent to sexual or even political matters is basically blocked. Creative content ideas that walk the line between uncouth and commonly shared experiences/truth cannot even be discussed with the agent. I haven't explored taboo topics like talking about political figures or racism, but the models avoid touchy subjects in general. Has anyone found a solution?

To harden your content pipeline against hallucinations while keeping the LLM’s role minimal, you need to fundamentally shift your architecture: Move from an "Agentic" paradigm to a "Deterministic Orchestration" paradigm.

In an agentic workflow, the LLM decides what to do next, writes code, or chooses tools. This is where hallucinations break pipelines (e.g., the LLM hallucinates a file path, invents an API parameter, or gets stuck in a loop).

To minimize the LLM's role, treat it as a dumb, highly constrained function rather than an autonomous agent. The surrounding code (Python, Node, etc.) should handle all logic, state, and execution.

Here is the blueprint for hardening your video and social media pipeline.

---

1. The Core Rule: Never Let the LLM Write Code or Control Flow

The fastest way a pipeline breaks is when an LLM hallucinates a Python script to run FFmpeg or call a social API.

• The Fix: The LLM should never generate executable code. It should only output data (text, parameters, JSON). Your deterministic code takes that data and executes the actual tools (FFmpeg, TikTok API, YouTube API).
• No Tool-Calling Loops: Do not use ReAct (Reason + Act) loops where the LLM decides which tool to call. Hardcode the sequence of tools in your orchestrator.

2. Constrain the LLM’s Output (Zero-Hallucination Formatting)

If you ask an LLM to "write a script and give me the hashtags," it might format it differently every time, breaking your parser.

• Use Strict Structured Outputs: Use OpenAI’s Structured Outputs, or libraries like Instructor (Python) or Zod (TypeScript). Force the LLM to output strictly typed JSON.
• Example: Instead of a free-text prompt, define a Pydantic model:

  class VideoScript(BaseModel):
      hook: str = Field(max_length=150)
      body: str = Field(max_length=800)
      cta: str = Field(max_length=100)
      visual_prompts: list[str] = Field(min_items=3, max_items=5)
  

• Constrained Decoding: If using open-source models, use libraries like Outlines or Guidance to guarantee the output matches a JSON schema at the token level. It makes malformed JSON mathematically impossible.

3. Implement Deterministic Guardrails & Validators

Never pass LLM output directly to the next step in the pipeline. Put a "bouncer" (validation code) between every LLM call.

• Length/Token Checks: If the LLM is supposed to generate a 60-second script, check the word count. If it’s 400 words (too long for 60s), truncate it deterministically or trigger a strict retry.
• Content Filters: Run the generated script through a lightweight toxicity/brand-safety classifier before sending it to the TTS (Text-to-Speech) or Video generator.
• Asset Validation: If the LLM generates an image prompt for a scene, check it against a blocklist of banned words before sending it to Midjourney/Flux/Runway.

4. Harden the Video & Asset Generation

Video generation APIs (Runway, Pika, Sora, HeyGen) frequently fail, timeout, or return artifacts. The LLM cannot fix this; your code must.

• Fallback Assets: If an AI video generation fails or times out, your orchestrator should deterministically fall back to a pre-approved stock video or a static image with a Ken Burns effect.
• Deterministic Assembly: Use FFmpeg via code to stitch the TTS audio, generated video clips, and captions together. Do not ask the LLM to figure out the FFmpeg commands. Hardcode the FFmpeg wrapper in your application.
• Caption Syncing: Use deterministic tools like Whisper (on the generated TTS audio) or StableFast3D to generate exact word-level timestamps for captions, rather than relying on the LLM to guess when words are spoken.

5. Harden the Social Media Submission

Social APIs (TikTok, Instagram, YouTube Shorts) are notoriously strict and change frequently.

• Hardcoded API Wrappers: Write strict, typed wrapper functions for each social platform. The LLM should only provide the metadata (Title, Description, Tags).
• Data Mapping: Your code maps the LLM's metadata to the exact API requirements. For example, if the LLM generates 35 hashtags, your code deterministically truncates it to the platform's limit (e.g., 30 for Instagram) and formats them correctly.
• Pre-flight Checks: Before submitting, your code should verify the video file size, aspect ratio (9:16), and codec (H.264) match the target platform's exact API specs.

6. Human-in-the-Loop (HITL) for the "Last Mile"

Because this is going to public socials, a 100% autonomous pipeline is a business risk. Minimize the LLM's role by making the human the final agent.

• Draft State: The pipeline should generate the video and save it to a staging area (e.g., an AWS S3 bucket or a private Discord/Slack channel).
• Approval Gate: The pipeline pauses. A human reviews the video and the auto-generated caption.
• Execution: The human clicks "Approve" (via a simple UI, Slack button, or email reply), which triggers the deterministic code to publish it to the social APIs.

---

Summary Architecture: The "Dumb LLM, Smart Code" Pipeline

Here is what your hardened workflow should look like in practice:

1. Input: User provides a topic (e.g., "3 tips for saving money").
2. Orchestrator (Code): Sanitizes input, checks against database for duplicates.
3. LLM Node 1: Generates script. (Strict JSON schema enforced. Output validated for length).
4. Orchestrator (Code): Passes script to TTS API. Generates audio file.
5. Orchestrator (Code): Uses Whisper to generate word-level timestamps.
6. LLM Node 2: Generates visual prompts for B-roll. (Strict JSON schema enforced. Checked against blocklist).
7. Orchestrator (Code): Sends prompts to Video Gen API. (If it fails, falls back to stock footage).
8. Orchestrator (Code): Runs hardcoded FFmpeg script to merge Audio + Video + Captions.
9. LLM Node 3: Generates Title, Description, and Tags. (Strict JSON schema enforced).
10. Orchestrator (Code): Validates metadata against platform limits. Packages video and metadata.
11. HITL Gate: Sends package to Slack/Discord for human approval.
12. Orchestrator (Code): Upon human approval, calls hardcoded TikTok/IG/YT APIs to publish.

By treating the LLM strictly as a text-and-data extraction engine, and using deterministic code for all orchestration, validation, and execution, you effectively eliminate hallucination-induced pipeline breakages.

https://preview.redd.it/du0rhekdwvgh1.png?width=1561&format=png&auto=webp&s=ae9072c5206d2a6091d3df85f53d90794351fd44

Hey Guys, I stumbled on this webpage while trying to search Opencode Go plan, Commandcode is very cheap then Opencode. Is anyone here tried this plan? Whta is your experience on this? Please, thank you!

DeepSeek quietly (then very loudly) pushed the official **DeepSeek-V4-Flash-0731** over the past day or two, and the community is going crazy for good reason.

### Quick specs

- **284B total / 13B active** MoE

- Native **1M context** (same as V4-Pro)

- MIT license, open weights now on Hugging Face (`deepseek-ai/DeepSeek-V4-Flash-0731`, ~167GB FP4/FP8 mixed)

- Pricing unchanged and still absurdly cheap: **$0.14 / $0.28** per 1M input/output tokens (cache hits ~$0.0028, a 98% discount)

- Supports thinking modes (High / Max), Responses API format, and is fully adapted for Codex

- Available on DeepSeek’s own API (public beta), OpenCode (including free tier), Cline (they made it free), etc.

### The big story: Agent capabilities got *massively* upgraded

DeepSeek re-did the post-training. Same architecture/size as the April preview, but the agent scores jumped hard and now beat the old **V4-Pro-Preview** across a bunch of agent benchmarks.

Highlights floating around:

- Terminal-Bench 2.1: **82.7** (was ~56.9 on the preview — +25+ points)

- Strong gains on other agent/tool-use suites (DSBench, Toolathlon, etc.)

- Artificial Analysis Intelligence Index: **50** (up ~10 points from the original Flash). That puts it in the top 3 open-weights models on their board, competitive with recent Gemini Flash / GPT-5.6 Luna-tier models on intelligence while being dramatically cheaper.

People are calling it one of the best performance-per-dollar models in its class right now.

### LMArena / Arena.ai results (the part everyone’s screenshotting)

Arena.ai (formerly LMArena) has been posting updates:

**Frontend Code Arena** (the one lighting up timelines):

- DeepSeek-V4-Flash-High: **#7 overall**, **#3 open**, score **1586**

- Categories: #4 Consumer Product, #6 Reference-based Design / Data & Analytics / Gaming, #7 Brand & Marketing

- +154 pts vs the Flash High Preview, and even +121 pts vs the old V4-Pro Preview

It literally reshaped the Pareto frontier on that board for performance-per-dollar.

Earlier (April) Text/Code Arena numbers for the original preview were more modest (Flash thinking was around #10 open / #47 overall on Text Arena). The new post-training version is clearly a different beast on agentic/coding tasks.

### Community vibes from popular tweets

- People running tens of millions of tokens for a couple of dollars (or less) thanks to the cache hits

- “This is the first flash model that actually feels SOTA”

- OpenCode, Cline, and others racing to integrate it (some making it free)

- Lots of “Chinese open models aren’t just catching up anymore” energy

- Some skepticism about vendor-harness numbers (fair), but independent Arena + Artificial Analysis numbers are looking strong

- Local runners already trying the new weights

### Where to try it

- chat.deepseek.com (Expert/Instant Mode)

- Official API (model name `deepseek-v4-flash`)

- Hugging Face weights

- OpenCode / Cline / various third-party providers

This feels like DeepSeek doing the classic move: ship a very strong, very cheap open model that punches way above its active-parameter weight, especially for agents and coding. The gap between “Flash” and “Pro” on agent tasks has basically collapsed after this update.

Anyone already deep into long agent runs or heavy coding workloads with it? Drop your experiences / cost numbers / failure modes below.