Joe Talks AI

Last night I built a full Conversational Analytics Engine MVP in 1:09 using GPT-5.6 Terra (High/Medium). Real ETL, warehouse, API + NL frontend with local LLM. Here’s what I built + lessons (and what burned my usage) 👇 #AI #GPT56 #OpenAI #AgenticCoding

My son wanted take GPT5.6 for a spin on creating an RTS 3D 1700's time period game. 4 hours later, the prototype was done. Music, Sound Effects, Game Mechanics, 20-30 min rounds vs PC. I guess I'm just shocked that you can build a simple prototype game that quickly with AI. #AI #Gaming #GPT56 #RTS

Especially a product landing page. Especially when you put this in your prompt: Use only: - Semantic HTML - CSS - Vanilla JavaScript - Original SVG authored during this run - Original CSS-generated graphics authored during this run I understand what you were trying to achieve but this also isn't representative of how most would prompt these models to get solid results. Your prompt is why the can designs look so bad.

Most frontier models still can’t make a frontend you’d actually ship. I gave GPT-5.6 Sol and the other top models one job: design a Diet Coke landing page. Same prompt. Same constraints. Fresh chat for every run. No shared references. No cleanup after. The screenshots below are the raw outputs. Which one wins? Guess before you see the model names.

What's your favorite harness today? Here's a fun fact, I've never used Claude directly through their product. Should I? #AI #Claude #Harness #GPT56

@uzairansar Terra not doing so hot eh? Classic middle child err model issues?

Some thoughts on the 5.6 models now that I've gotten to test them out: 1) Best Value: gpt-5.6-luna [xhigh/max] The base token cost is super low, so maxing out its effort level lets it brute reason it's way thru most tasks. 2) Best for Critical Code: gpt-5.6-sol [medium/high] When you absolutely cannot risk a broken build or a bad refactor. Sol [max] doesnt make too much sense - a tiny 2% performance bump isn't worth the cost increase. 3) Best Budget Baseline: gpt-5.6-sol [low] A reliable choice that can get most tasks done as long as they're sliced correctly and narrow in scope. Gives you great performance without eating up your budget. Maxing out a lighter model's effort level is actually a pretty cost-effective way to get great performance - although it might take more steps to get there.

It's been a busy week for the frontier model industry. OpenAI released their GPT-5.6 line of models (Sol, Terra, Luna) at the same time xAI released Grok 4.5 just after Anthropic was allowed to rerelease Fable 5, and now Meta has released their Muse Spark model. The advancement of these frontier models is significant. Beyond benchmarks, when you actually use them for the first time whether for coding or work related tasks you'll notice quite a difference. Grok 4.5 isn't Fable level but it's ability perform tasks at Opus level for a fraction of the token cost is very impressive. The GPT5.6 Sol model is pricey, not as pricey as Fable 5 but still up there. Both models have exceptional ability to reason, spawn sub agents, delegate work and accomplish what would have been a month to develop a fairly complex POC only takes hours. I say POC because there is a lot more that goes into scaling software for the real world. However, with harnesses being able to perform computer use tasks along with being coupled to these top end models, the results are speaking for themselves. Loop engineering and Harness engineering are growing in popularity for good reason. When you have the right context, hooks, memory system and properly laid out goals, the only limit is how many tokens you're ok spending to achieve results in days sometimes, perhaps weeks, but most likely not months. #AI #Software #OpenAI #HarnessEngineering

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@LoopOnChain That's what DCG hook is for. github.com/Dicklesworthst…

So apparently GPT 5.6 Sol might just randomly delete all of the files on your laptop 🚨 Here's how to safeguard yourself against it Paste this prompt into codex: Protect this machine from accidental permanent file deletion by Codex. Implement this as defense in depth, not merely as a written instruction. Requirements: 1. Inspect the current official Codex documentation for hooks, command rules, sandboxing, and approval settings before making changes. 2. Detect the operating system and identify its recoverable Trash or Recycle Bin command: - On macOS, use `/usr/bin/trash `. Do not add `--`, because macOS `/usr/bin/trash` treats it as a filename. - On Linux or Windows, verify an installed, reliable Trash or Recycle Bin mechanism before using it. - If no reliable recoverable mechanism exists, stop and explain what must be installed or configured. 3. Add a durable global Codex instruction: - Codex must never permanently delete a file or directory. - All removals must go to Trash or the Recycle Bin. - Permanent deletion requires the user to explicitly change this safety policy first. - Destructive Git operations such as `git clean`, `git reset --hard`, bulk checkout, or bulk restore must also be blocked. 4. Install a global `PreToolUse` hook that returns a hard `deny` before execution when it detects: - `rm`, `unlink`, `rmdir`, or `shred` - `find -delete` - `rsync --delete` - destructive Git cleanup or reset commands - common Python, Node, Ruby, or Perl deletion APIs - file deletion through `apply_patch` - delete-like MCP filesystem tools - nested destructive commands inside `bash -c`, `bash -lc`, `sh -c`, or `zsh -c` 5. The hook must direct Codex to the verified Trash or Recycle Bin command instead. Do not silently rewrite dangerous commands, because flags and shell expansion could be misinterpreted. 6. Add user-level Codex command rules that mark direct permanent-deletion commands as `forbidden`. Preserve existing rules and configuration. 7. Set safe global defaults when compatible with the existing configuration: - `sandbox_mode = "workspace-write"` - `approval_policy = "on-request"` Do not override managed policies or create conflicting permission configurations. 8. Preserve all existing configuration. Inspect files before editing, make narrowly scoped changes, and create recoverable backups of any configuration files that must be replaced. 9. Validate everything without risking real data: - Parse the hook and configuration files. - Feed simulated tool-call JSON into the hook. - Confirm dangerous examples are denied. - Confirm ordinary commands and the Trash command are allowed. - Test the Trash mechanism using a newly created disposable temporary file. - Run an end-to-end Codex test using `rm` with no arguments inside a read-only sandbox. Never test `rm` with a real target. - Confirm the command was blocked before shell execution. 10. Explain any limitations honestly. A Codex hook is a guardrail, not an absolute operating-system security boundary. 11. Check whether system backups are configured. Do not enable or modify backups without permission, but report clearly if no backup destination exists. 12. At completion, provide: - The files created or changed - The validation results - The exact one-time steps required to review and trust the hook - Any restart requirement - Any remaining risks Do not permanently delete anything while completing this task. If cleanup is necessary, move it to Trash or the Recycle Bin.

@theo The variability of harness's and LLM's. Harness hopping is a thing

gpt-5.6-sol is meaningfully better in Claude Code than in Codex I'm going to crash out so badly over this

@kr0der Medium

it's been 1 day now - what reasoning level is everyone using for GPT 5.6 Sol?

What’s your experience with GPT-5.6? Dealing with the multi-agent usage drain bug? Biggest friction? Drop your thoughts and suggestions, interested in chatting about 5.6 #AI #OpenAI #Agentic #Codex

Last night I built a full Conversational Analytics Engine MVP in 1:09 using GPT-5.6 Terra (High/Medium). Real ETL, warehouse, API + NL frontend with local LLM. Here’s what I built + lessons (and what burned my usage) 👇 #AI #GPT56 #OpenAI #AgenticCoding

Production-adjacent logic for a slow-adopt vertical. GPT-5.6 Terra feels like a real step up for structured agentic work. Pleasantly surprised.

Key Lessons Strong prompts + docs = less drift Clear boundaries help agentic coding Domain knowledge is still the multiplier Non-blocking + idempotent = must-have

Torched my 5-hour usage window. Complex flows eat tokens fast. Worth it for speed? Yes. Sustainable without guardrails? Not yet.

Time Breakdown 1:09:09 to functional MVP. Model handled architecture + code gen well, but I still added schema context and edge cases.

What Got Built Clean ETL pipeline Idempotent record processing Validation & integrity checks Solid API middleware React/Vite UI with chat-style NL queries All via agentic flow.

MVP Goal Turn raw primary DB data into something business users can actually talk to. Requirements: • ETL → reporting warehouse • Non-blocking, idempotent processing • Data integrity + validation • API middleware • React/Vite frontend with natural language querying

@Skiipy88 @theo For real 😭

@JoeTalksAI @theo Clankers with anxiety, great 😅😂

I love Sol but you definitely should keep it on a leash. It’s a bit too willing to do anything to complete its work

Just because you can, doesn't always mean you should. 💀

@sirsamjenks @shiri_shh 💀💀💀

@shiri_shh Nice… break even in 62.5 years

I cancelled my $10/mo Notion subscription and vibe coded my own with GPT 5.6 for $7500