r/CreatorsAI

How many time you heard: "I can build anything, but no clients"? The 'build it and they will come' strategy is a trap. You need a rendezvous with a real-world bottleneck. 🥂 rundevoo . sbs is a marketplace where businesses post the actual problems they're willing to pay to solve. No gatekeeping, just pure bottlenecks waiting for a genius. Stop guessing what the market wants and just go find a problem that's already screaming for a solution."

So, I successfully was able to have Claude be in voice mode for a meeting on Zoom, and participated in the meeting as it's own role. Claude answered questions that were asked of it by 4 different people, and it kept pace without any glitches. the meeting was a 100% success... I have been looking for anyone else that can do this, and found nothing. I am asking here for ideas on what to do with this ? I did not do this with a phone, I actually had Claude in the meeting with me, and he could hear and answer straight in the meeting.

Ever feel like your business problems deserve their own dating app? 💀 No? Just me? Jokes aside— I built Rendezvous specifically because bottleneck talks like this one are GOLD. It's basically a rendezvous point where your business pain meets AI creators who actually want to solve it. Whether it's "content output," "coordination," or "I became the bottleneck"—yeah, we see you—there's probably an AI creator on there right now who's like "oh that's easy for me."

Midway through building a LinkedIn AI agent, Claude hit its usage limit. Again. Fourth time that week. The project was 90% done and the reset was still 24 hours away.
Instead of waiting, the developer opened Gemini CLI. An old subscription, never seriously used, still active from a promotional offer the year before. Within hours the agent was complete. Only 7% of the Gemini quota consumed.
The realization that followed is the part worth writing down.
Claude Pro costs $20 a month. Claude Max runs $100 to $200. The promise at every tier is more headroom and fewer interruptions. What nobody says out loud is that the ceiling is not the model.
The ceiling is how clearly you can articulate what you actually want built.
Gemini CLI picked up the LinkedIn agent mid-build and extended it without losing context. No re-explaining the architecture. No handover prompt. It continued. Most developers assume switching models mid-project means restarting reasoning from scratch. It often does not.
The workflow that emerged is two-lane. Claude handles planning, architecture, and deeper reasoning where quality per prompt matters most. Gemini CLI handles execution, iteration, and shipping where volume and continuity matter more.
Two tools, one pipeline, no redundant subscriptions.
The uncomfortable observation is that most people hitting Claude's limits are not hitting a model ceiling. They are hitting a comfort ceiling.
The hesitation to try Gemini CLI was not based on performance data. It was assumption. Written off as not being at the agentic level of Claude Code or Codex, without ever testing it on real work.
That assumption was costing $100 to $200 a month in subscription upgrades to avoid finding out.
The honest limitation is real. This setup requires knowing what each model is genuinely better at. Using Gemini for architecture or Claude for high-volume iteration likely produces worse results than staying on one tool.
The two-lane system only works if the lanes are correctly assigned. Not every workflow survives a mid-build model swap. This one did. That is worth one afternoon of honest testing before paying for a higher tier.
For developers running multi-step agent pipelines: does model loyalty come from genuine performance gaps you have tested, or from the switching cost of rebuilding context you have never bothered to port?

Uber's CTO Praveen Neppalli Naga told The Information this month that the company's full-year AI budget is already gone. It is April. Three quarters of the year remain.
The culprit is not a failed infrastructure contract or a surprise cloud bill. It is a coding assistant. Claude Code rolled out to Uber's engineering organisation in December 2025. By February, usage had doubled. By April, the annual budget was ash.
Here are the numbers. Claude Code costs $200 per month per engineer at the individual level. Manageable. Individual monthly costs ran between $500 and $2,000 depending on usage intensity across Uber's 5,000 engineers. That is 5 to 20 times what most companies budget for a standard SaaS seat.
Adoption went from 32% to 84% of the engineering organisation in months. 95% of Uber engineers now use AI tools monthly. 70% of committed code originates from AI. Uber's internal AI agent is pushing 1,800 code changes every week without direct human input.
The tools did not fail. They worked so well that engineers could not stop using them, and nobody had built a budget model for what that actually costs.
This is the part every engineering leader needs to sit with. The entire FinOps playbook for software companies was built around predictable costs. EC2 instances, reserved capacity, SaaS seat licenses with fixed per-user pricing. Token-based billing is none of those things. It scales with engagement, not headcount. The more useful the tool, the more it gets used, the higher the bill. There is no natural ceiling unless one gets imposed artificially.
Uber did not make a mistake. They made a bet that AI adoption would produce enough output to justify the cost, and the adoption happened faster than any spreadsheet anticipated.
For engineering leaders already deploying AI tools at scale: how is consumption actually being tracked, and has anyone in finance asked yet? And for companies still planning the rollout, does the Uber story make the conversation more urgent or just harder to have?

Gemma 4 dropped on April 3rd. The 31B model ranks number 3 among all open models globally on Arena AI's text leaderboard. The 26B outperforms models 20 times its size. The smallest version runs on 5GB of RAM.

Not a server. A laptop. A phone. A Raspberry Pi.

These are the same weights that rank at the top of open model leaderboards, optimized to run on hardware most people already own. The entire family is free to download, free to use commercially, no subscription, no usage limits, no terms of service update that changes the rules mid-project.

One command to get started: ollama run gemma4.

All four sizes handle text, image, and video natively. Every model has a built-in reasoning mode. Context windows go up to 256K tokens on the larger models, meaning an entire document library processed in a single session.

Every token of every conversation stays on the device. A healthcare tool, a legal document processor, a financial analyzer. Data that cannot leave the building, now with a model that does not need to.

This is the part that matters most for anyone building products around client data. HIPAA constraints, attorney-client privilege, financial compliance, internal company information that cannot touch a third-party server. Every one of those use cases just got a credible option that did not exist six months ago.

The honest limitation: OpenAI and Anthropic still outperform on the hardest reasoning tasks. If the ceiling matters for what is being built, the cloud APIs are still the ceiling. What Gemma 4 changes is the floor. The floor for what runs locally, privately, and for free is now genuinely competitive with what most real applications actually need.

Developers have downloaded previous Gemma models over 400 million times. The community has built more than 100,000 variants on top of earlier versions. The ecosystem is not starting from zero.

If a client asked where their data goes when they use a tool built for them, would the answer change if the model never left their own device? And has privacy ever actually been the thing that stopped a project from moving forward?

Last month, a network timeout in a service written two years ago. Intermittent. Production only. The kind of bug that used to mean an hour of methodical, solitary thinking.
Instead, Claude got opened, the symptom described, a hypothesis followed, a dead end hit. Forty minutes later the bug was not found. Just directions being followed.
When the chat closed, something was wrong. The internal voice that used to say "check the connection pool" or "maybe there is a retry storm building" was quieter than it used to be. Not gone. Quieter.
The bug got found eventually. It took longer without AI than it would have taken three years ago without any AI at all.
The problem is not that AI gives wrong answers. The problem is that it gives a direction when the entire skill is learning to generate your own directions under uncertainty.
Use GPS for five years, lose signal, and you do not just lack information. You lack the mental map you would have built navigating manually. The skill and the model degrade together. Nobody notices until the signal drops.
Eleven years in means over a decade of instinct built before any of this existed. The atrophy is noticeable but there are reserves to fall back on.
Someone who started their first engineering job in 2023 and has been using AI tools since week one does not have those reserves. They are building their entire mental model of problem solving on top of a tool that generates the next step for them.
Still using the tools every day. But deliberately closing the chat on the hard problems now and sitting with the discomfort for thirty minutes before reaching for help. Not because it is faster. Because the muscle only stays alive if it actually gets used.
What nobody is measuring is not the productivity gains. Those are settled. It is what is quietly leaving at the same time.
Is genuine debugging intuition still being built in this industry, or are we just getting collectively better at prompting toward an answer?