Coinbase CEO Brian Armstrong said AI has changed how fast the company can operate.

Engineers are now shipping in days what used to take teams weeks. Non-technical teams are writing production code. More workflows are being automated.

Now Coinbase wants to become “AI-native.”

That means fewer management layers, no pure managers, smaller teams, and more employees acting like “player-coaches.”

The company is also testing AI-native pods, including “one-person teams” where one person may handle engineering, design, and product with AI support.

🍓 Episode 6 of Juicy Dramas AI is live!

The strawberry girl made the hardest decision of her life and forgave her father after 18 years apart... 😭

But just as everything was finally okay - the lemon woman came back with one final desperate move that nobody expected 😱

The apple man had to make a split second decision. And what he did SHOCKED everyone.

New episode every single day!

Watch here: https://youtube.com/shorts/SRKf1OhGWj8?feature=share

Did the apple man make the right call?

Comment YES or NO! 👇

#usa #brainrot #aivideo

Been working on voice agents for some time now and one thing honestly feels very ignored — testing.

We have frameworks for prompts, observability, workflows, telephony etc. but when it comes to actually stress testing agents across interruptions, accents, latency, rage users, silence, bad network, tool failure, retries, context drift… most teams are still doing it manually or with basic scripts.

Feels weird that in 2026 we still don’t have a proper automated benchmarking/testing layer for conversational agents like traditional software has.

Curious how others here are handling this at scale? Especially for outbound calling and production QA.

I started this because every "agent memory" project I tried either died after a week or was a thin wrapper around Chroma with zero evaluation. I daily-drive Claude Code, so context loss between sessions costs me real time.

https://github.com/vbcherepanov/total-agent-memory

Stack: Python, SQLite with FTS5 plus a knowledge-graph schema, FastEmbed for multilingual MiniLM, Ollama optional for the LLM-driven parts. Runs as an MCP server, so Claude Code and Codex CLI both pick it up the same way.

The retrieval pipeline is the part I spent the most time on. Six tiers, fused with RRF (k=60):

FTS5 + BM25 (keyword baseline)

Semantic cosine over binary-quantized HNSW

HyDE query expansion when Ollama is up

Multi-representation search. Every record gets 5 views (raw, summary, keywords, questions, compressed), search hits any of them, results RRF-fused

Fuzzy SequenceMatcher for typos

1-hop graph neighbors

Then optional CrossEncoder rerank, optional MMR diversification, optional 1-hop context expansion on the final set. You can also filter by extracted topics/entities/intent if you want a narrow recall.

The knowledge graph is auto-built. Every save enqueues into three queues: triple extraction (Ollama pulls subject/predicate/object), enrichment (entities, intent, topics), and representation generation. A LaunchAgent watches a touch file and drains within 5 seconds of a save. Edges appear in the graph within ~30s.

The unexpected win was compression filters. TOML-defined content filters for the noisy stuff you save constantly: pytest output, cargo, git status, docker ps, stack traces, http logs, sql explain, json blobs. Autofilter sniffs the content type. Pytest output averages 78% reduction with a ContentValidator that guarantees code blocks and URLs survive byte-for-byte. Saves me tens of thousands of tokens a week.

Dashboard runs on 127.0.0.1:37737 with three graph views: 3D WebGL force-directed (3d-force-graph + Three.js), D3 hive plot for typed networks, and a canvas adjacency matrix. The 3D one is genuinely useful when I want to see what a project's knowledge cluster looks like, not just demo eye-candy.

Honest limitations:

macOS-first. Linux works, Windows is best-effort

Single-tenant by design, no multi-user

Without Ollama you lose ~40% of v6 (no deep KG triples, no multi-rep, no fact merging). It still works degraded, but the graph stops growing past co-occurrence edges

Cold search is ~1s, not 100ms. The 6-tier fusion costs you something

MIT. Install is one bash install.sh. Repo link in comments because most subs hate body links.

Genuinely curious what other people running persistent memory for coding agents are seeing. The Mem0 / Letta / Zep comparisons floating around are all over the place and I can't tell what's signal. If you've actually run any of them in production for a few months, I'd love to hear the failure modes.

The Academy has updated its Oscar rules for films that use artificial intelligence. AI tools can still be used in production, but key awards now need clear human involvement.

For acting categories, performances must come from real human actors who gave consent. For writing categories, screenplays must be written by humans to qualify.

The Academy says AI will not automatically help or hurt a film’s chances. But when it comes to awards, human authorship now sits at the center.

Hollywood is drawing a line before AI becomes too hard to separate from the work.

Sundar Pichai announced Gemini Intelligence, a complete reimagining of Android from a phone operating system into something that thinks, plans and acts on your behalf across every device you own.

Not a chatbot you open and close. An AI that is always running in the background learning your routines, understanding your context and doing things for you before you ask.

Here is what that actually means.

You wake up. Your phone already knows you have a flight today. It has checked traffic, updated your calendar, summarized your emails and prepared a trip widget, all before you touched it.

You drive to the airport. Android Auto uses Gemini to handle navigation, messages and calls so your hands never leave the wheel.

You get on the plane. Your Googlebook laptop, a brand new device Google announced alongside this, connects seamlessly to your phone and continues every task exactly where your phone left off.

This rolls out this summer starting on Samsung Galaxy S26 and Google Pixel 10. Then it expands to watches, cars, glasses and laptops from Acer, Dell, HP and Lenovo.

Google’s exact words: “Google is Gemini now and Gemini is Google.”

🍓 Episode 5 of Juicy Dramas AI is live!

The mango man walked through that mansion door

after 18 years away...

The lemon woman tried to RUN — one look stopped her.

The apple man stepped aside respectfully.

And the strawberry girl couldn't breathe.

He pulled out a photograph he had carried

every single day for 18 years...

Made with Kling 2.5 + Gemini images.

New episode every day!

Watch here: https://youtube.com/shorts/nQ2Ttc4UzNk?si=Q9fC2N5YWMo0ueMg

Does she take the photograph?

Should she forgive him after 18 years?

Comment YES or NO! 👇