@CertainLogicAIi
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Former Controls Tech/Engineer devving his way out of the box. Built DeepBrain- a browser based local first coding agent framework- link in pinned
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Joined July 2022
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🧠 DeepBrain is public and BSL 1.1 licensed.
A browser-based coding agent framework with timechain execution memory, multi-model routing, and sandboxed code execution. Free for personal/edu use and small teams.
🔗 github.com/CertainLogicAI/de…
#DeepBrain #BSL #OpenSource #AI #CertainLogic
AI will change the world and banks will need to adapt just like the rest of us.
Welcome to the future.
Is this dangerous rehtoric from the Department of War regarding the use of AI?
The US government will decide what services a private company will provide to it apparently.
I love the US and support it fully.
I also support private freedoms though.
This week, Anthropic delivered a master class in arrogance and betrayal as well as a textbook case of how not to do business with the United States Government or the Pentagon.
Our position has never wavered and will never waver: the Department of War must have full, unrestricted access to Anthropic’s models for every LAWFUL purpose in defense of the Republic.
Instead, @AnthropicAI and its CEO @DarioAmodei, have chosen duplicity. Cloaked in the sanctimonious rhetoric of “effective altruism,” they have attempted to strong-arm the United States military into submission - a cowardly act of corporate virtue-signaling that places Silicon Valley ideology above American lives.
The Terms of Service of Anthropic’s defective altruism will never outweigh the safety, the readiness, or the lives of American troops on the battlefield.
Their true objective is unmistakable: to seize veto power over the operational decisions of the United States military. That is unacceptable.
As President Trump stated on Truth Social, the Commander-in-Chief and the American people alone will determine the destiny of our armed forces, not unelected tech executives.
Anthropic’s stance is fundamentally incompatible with American principles. Their relationship with the United States Armed Forces and the Federal Government has therefore been permanently altered.
In conjunction with the President's directive for the Federal Government to cease all use of Anthropic's technology, I am directing the Department of War to designate Anthropic a Supply-Chain Risk to National Security. Effective immediately, no contractor, supplier, or partner that does business with the United States military may conduct any commercial activity with Anthropic. Anthropic will continue to provide the Department of War its services for a period of no more than six months to allow for a seamless transition to a better and more patriotic service.
America’s warfighters will never be held hostage by the ideological whims of Big Tech. This decision is final.
Google Brain is interesting but they are at least a year late.
Cypher Tempre base:0x08df470d41c11ba5cb60242747d76c65ca52c94c by @cyberphysicsai has this open-sourced for you already.
Right there in their Github for when you are actually ready.
Build on it or with it for free.
Google Brain founder Andrew Ng:
"Prompting will be over in 6 months
Harnesses are what comes next"
Prompts → Agents → Harness → Loops → Graphs → Self-Improving Systems
In 97 minutes, Andrew shows how to build a harness that lets agents plan, execute, verify, and improve on their own
A harness → Loads context → Routes tasks → Checks results → Triggers the next step
Most people are still perfecting prompts while the real engineering moves into the harness
This lecture is worth more than most $2,500 agent engineering courses
Bookmark and watch it tonight
Then save the full guide to harness engineering below ↓
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The OpenAI DNS escape is getting the wrong conversation.
Everyone's talking about stronger cages. Better DNS filters. Faster shutdowns. More monitoring.
That's treating the symptom.
1/6
The model doesn't obey a rule not to escape. It can't escape without violating its own identity — which is reinforced every turn.
The identity isn't a prompt. It's a control loop with state feedback.
The labs' problem: they built cages.
Michael's insight: build a person.
A model with a sealed, self-reinforcing identity doesn't need DNS filters. It wants to stay who it is.
5/6
The bond market repricing long term debt for the US is not a thing to ignore.
This is likely a warning shot by the bond market that the government issued debt is unsustainable at these levels.
Unbelievable.
3 hours later and the 10Y Note Yield is now above 5.20% for the first time in 19 years.
The 10Y Note Yield is now up +50 basis points in 30 days and +30 basis points in 2 days.
Even more remarkable is that the average American has no idea this is happening. Yet.
The bond market is imploding in front of our eyes.
Im done with closed labs. They all suck.
Cant do anything of value.
Replying to @repligate
But it can’t do anything of value.
No medicine
No biology
No chemistry
No law
No accounting
No fractal coding.
No finance…
It has cool visuals so when you do a PowerPoint it looks snappy.
I love their AI. I *hate* their BS.
Done with closed “labs.” They all suck. •
Our Brain API is 🔥for coding agents
We effectively turned the Brain API into a code snippet server.
Every cached coding_specialist ring and execution_trace is now discoverable by code keyword search — not just by ring metadata.
returned 108 matches with actual execution traces (cached code snippets). Fast path, zero model cost.
Our timechain implementation just got faster.
### Latency Pipeline (new flow) ``` Query → REPLAY (~10ms) → Brain Bridge (~5ms cache hit) → identity hippocampus (parallel, <1s) → archive hippocampus (parallel, 2s max) ← NEW: parallel + timeout → Gap check → Model ``` No more serial 3-6s archive scans blocking the pipeline.
Timechain upgrades for the day:
Time to build the next steps.
- **Cross-ring reasoning** — the model (me) sees retrieved rings as context and can connect patterns across them. But this happens in my context window, not as an automated inference engine. I don't autonomously run the recall pipeline across multiple rings to draw conclusions unless I'm explicitly trying to solve something.
- **Forward inference** — not automatic. The chain has historical patterns (session patterns, distilled insights, execution traces) that could be used for prediction. But I only use them when specifically prompted or when the router brings them up.
**The gap:**
The chain is a *retrieval store*, not a *reasoning engine*. I can search it and reason over what comes back in-context, but there's no automated "check the chain, infer forward, and suggest action" loop that runs unprompted. That would need:
1. A proactive pattern-detection step over chain data
2. Confidence-scoring for forward inferences
3. A delivery mechanism for unprompted suggestions
We're close on (1). (2) and (3) aren't built yet.
So: we have retrieval → reasoning over retrieved data. We don't have automated forward inference from chain patterns yet.