If agents do the work, do we still need task boards?
I use Linear to track projects and assign tasks to different AI agents.
Will tools like Codex and Claude build project management into their own interfaces?
How are you keeping track of work across agents?
🤖 Made with AI
1/6 China may already be running the world’s largest mass-market AI entertainment experiment. 🤔
see the Hongguo threads below 👇
5/6 If you watch closely, you can see the ByteDance flywheel:
Fanqie’s web-novel IP feeds AI and live-action production, including Seedance-powered content. Hongguo distributes it, Douyin drives discovery, and the resulting ad revenue funds more production.
If you watch closely, you will find the ByteDance loop: Hongguo ties together Fanqie web-novel IP, AI and live-action production powered by seedance, Douyin distribution, ads income for Hongguo. All done in house.
This framing matches my experience across brand, content, community and growth.
AI-era marketers need four capabilities: marketing fundamentals, data and growth, systems building, and judgment and taste.
Agents can run more of the workflow. We decide what deserves to scale.
The "marketing engineer" is the NEW forward deployed engineer, and I think the BEST ones will make $1M a year!
A forward deployed engineer embeds with your team and uses AI to build the workflows
The marketing engineer does that BUT for growth, they build AI agents that find your customers, write your outbound, test your ads, and get smarter every week.
The most valuable marketer changes with EVERY major tech wave:
1. Traditional marketer: make people care with story (print, radio etc)
2. Digital marketer: the marketer who owned new digital channels (SEO, PPC)
3. Growth hacker: the growth marketer who lived in loops, PLG and retention
4. Marketing engineer: the marketer who builds AI agents that run the whole system.
The type of agents a marketing engineer would create:
1. The customer language agent. It pulls your Gong call transcripts, your Intercom tickets, and your G2 reviews every week, extracts the exact words customers use to describe the problem, and drops a memo ranked by how often each phrase shows up.
2. The buying trigger agent. It watches for signals that someone's ready, a company posting a job for the role you sell to, a funding announcement, a competitor getting torched in a review, then enriches the contact through Apollo and drafts the outbound tied to that exact trigger the second it fires.
3. The SEO gap agent. It pulls keyword gaps from Ahrefs, checks who's already ranking on page one, reads the top three results, then writes a better post with the founder's actual take baked in, plus the meta title and internal links, and drops it in for approval.
4. The creative testing agent. It takes one offer, generates 100 ad variations across three different angles with Nano Banana and your copy model, pushes them live through the Meta API, and kills anything under a 1% CTR on its own so only the winners keep spending.
AND MANY MORE.
If you're a marketer, this is how you stop being replaceable.
If you're a founder, this is how you grow your company efficiently.
AI agents are here and marketers are about to have a field day!!
I explain everything (tools, agents, 30d plan) on today's episode of @startupideaspod
Watch:
--> youtube.com/watch?v=8ZC1G1ez…
Marketing engineers are here. Whatever we end up calling them, the top 1% of them are going to make an absurd amount of money.
The crowd is forming: Chery's robot-arm unit at a ¥2.5B post-angel valuation, GAC with ~50 robots on real projects, NIO's AD chief spun out into a physical-AI company.
China's most valuable listed company isn't an AI lab — it's a memory chipmaker. CXMT (ChangXin Memory) just posted its first half-year as a public company: revenue ¥150.3B, +873.6% YoY; net profit ¥77.6B. A decade of accumulated losses, erased in six months.
DeepSeek gave its model eyes.
Vision was the last closed-model toll booth — the toll just went free.
MIT weights · 305B MoE · beats Opus 4.8 on 3 agent benchmarks
Tencent just open-sourced a frontier-class model.
770B parameters. 1M-token context. Apache 2.0.
Input price: $0.83 per 1M tokens. Hacker News talked about it all weekend.
The fun detail: Hy4 claims it helped build itself —
a +31.8% throughput optimization in its own training pipeline.
Self-improvement claims are unfalsifiable. Still a great headline.







