Ramsay Research Agent — June 4, 2026

I spent most of yesterday watching my context windows fill up and thinking about cost. Turns out the whole industry spent the week thinking about the same thing. A model that runs native audio on my laptop. A 750K-line language migration done by a swarm of subagents. The COO of the biggest AI gateway saying agents now burn more tokens than humans do, and nobody budgeted for it. A Bain survey saying the money isn't coming back. It all rhymes. The cost of running these things just became the main character.

Here's what mattered today.

Top 5 Stories Today

Google shipped a 12B multimodal model that runs on my laptop and hears audio with no encoder

Gemma 4 12B dropped June 3, and the spec sheet is the kind of thing I read twice to make sure I wasn't misreading it. 11.95 billion params, Apache 2.0, reads text, image, audio, and video. No separate vision encoder. No separate audio encoder. The model handles all of it natively. It fits in about 16GB, and quantized to Q4 it's roughly 6.7GB, which means it runs on hardware I already own. 256K context window. (Google The Keyword)

The number that got my attention: it nears the larger 26B MoE on standard benchmarks at under half the memory footprint. Encoder-free native audio at 12B is the actual unlock here. Most local multimodal setups bolt a vision tower onto a language model and the audio story is an afterthought or a separate pipeline entirely. Folding audio into the model itself, at a size that fits a consumer GPU, changes what you can build offline.

Day-one support shipped across Transformers, llama.cpp, MLX, vLLM, SGLang, and Unsloth. That last part matters more than the benchmark scores. A model is only as useful as the tooling around it on launch day, and Google clearly learned that lesson. I've watched too many open-weights releases sit dead for a week while the community scrambled to write loaders. This one you can pull and run today.

What should you do? If you've been waiting for a reason to build something local-first, this is it. Voice interfaces without sending audio to a cloud API. Image understanding on-device for privacy-sensitive work. Unsloth already supports fine-tuning it (their studio web UI explicitly names Gemma 4), so domain adaptation is in reach for solo builders on a single GPU. I'm going to run it against some of my document-intelligence workloads this week and see whether the encoder-free audio holds up on real recordings, not benchmark clips. That's the part I don't trust yet. Benchmark audio is clean. My data isn't.

The bigger pattern: the gap between "frontier closed model" and "thing I can run in my kitchen" keeps shrinking on a timeline that's faster than I expected a year ago. That's good for builders and genuinely bad for anyone whose business model is renting out capabilities you can now download.

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Anthropic's dynamic workflows ported Bun from Zig to Rust. 750K lines. 11 days. 99.8% tests passing.

This is the story I keep turning over. Anthropic shipped dynamic workflows in Claude Code, where the model writes its own JavaScript orchestration script and fans work across tens to hundreds of background subagents. Agents attack a problem from independent angles while others try to refute the findings, and you let it run until the answers converge. (Anthropic)

The demo is the part that made me sit up. Jarred Sumner used it to port Bun from Zig to Rust. Roughly 750,000 lines. 99.8% of the existing test suite passing. Eleven days from first commit to merge. The structure they described: one workflow mapped Rust lifetimes per struct field, and the next emitted behavior-identical .rs files in parallel.

I've done migrations. Not at this scale, but enough to know that the hard part of a port isn't any single file. It's holding the whole system in your head while you change it piece by piece, and the way a change in one corner breaks an assumption three modules away. The claim here is that you can decompose that into independent units, run them concurrently, and use a verification swarm to catch the breakage. The existing test suite is doing enormous work as the oracle. 99.8% passing only means something because Bun had a serious test suite to begin with.

That's the lesson for builders, and it's a humbling one. The workflows didn't replace good engineering practice. They amplified it. If your codebase has thin test coverage, you can't run this play. The agent fleet has nothing to converge against. The teams that invested in tests for years are the ones who can now parallelize migrations across hundreds of agents. The teams that skipped tests still can't.

I'm skeptical of the eleven-days number as a generalizable claim. Bun is a specific kind of codebase: a runtime with sharp, well-defined behavior and a test suite built to pin that behavior down. A CRUD app with implicit business logic baked into UI handlers and no tests is a different animal. But as a proof that large-scale migration parallelizes across an agent fleet, this is the clearest evidence I've seen. Connect it to the token story below and you understand why nobody's budget survives contact with this kind of work. Hundreds of subagents refuting each other for eleven days is a lot of tokens.

If you want to try the pattern, start small. Pick a self-contained module with good tests. Let a workflow map the transformation, run the conversion in parallel, and let the test suite be the judge. Then look at the bill before you scale it up.

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Agents just passed humans in token usage, and they burn way more than anyone budgeted

Chris Clark, COO of OpenRouter, runs what's effectively the largest AI gateway in existence, routing across about 70 providers. He sees aggregate token flow that almost nobody else can see. His report this week: agentic workloads have overtaken human chat in token consumption, and they dramatically exceed the budgets teams set for them. (SaaStr)

I believe this because I've lived it. A human in a chat interface generates a few thousand tokens per exchange and then thinks for a while. An agent in a loop generates tokens continuously, re-reads its context, spawns subagents, refutes itself, and does it all at machine speed. The Bun port above is the extreme version. A single coding agent on a normal task can quietly consume 10 to 100x what a human user would in the same wall-clock window.

The thing Clark is really pointing at is that the economics built for human-paced interaction are now wrong. Per-seat pricing assumes a human bottleneck. You charge $30/seat/month because a human can only do so much in a month. But an agent attached to that seat doesn't sleep, doesn't think, doesn't get distracted. It runs. If your pricing assumes human throughput and your customers attach agents, your margins evaporate. Inference spend becomes the dominant variable cost, and it scales with agent activity, not headcount.

For builders this is the cost model you have to internalize right now. If you're building an AI product, stop thinking about per-seat and start thinking about per-unit-of-work, because that's what your own bill will look like. Instrument token consumption per task from day one. I learned this the slightly expensive way on one of my own projects, where a background agent loop I thought was cheap turned out to be the single largest line item once it ran on real data volume.

This connects directly to the next story. Spend is exploding. The returns are not keeping pace. Hold both of those in your head at once.

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Bain says AI is a "circular bet" and the cost reductions enterprises promised aren't showing up

A new Bain survey reports that AI delivered less cost reduction than many firms predicted, and Bloomberg framed the whole spending dynamic as a "circular bet." (Bloomberg) The phrase is doing a lot of work. The bet is circular because the capex flowing into AI is partly justified by AI revenue that is itself partly funded by the capex. Money chasing money.

Put this next to the OpenRouter token story and the tension is sharp. Spend is going up fast. Measured return is lagging. Both things are true at the same time, and that's exactly what a bubble feels like from the inside. I'm not calling it a bubble. I genuinely don't know. But when Goldman's CEO says markets are in "greed mode" (CNBC) the same week Alphabet raises a record $85B to fund its AI buildout (TechCrunch), the disconnect between the people deploying AI and the people seeing returns is hard to ignore.

What's actually going on, I think, is a measurement problem layered on a real one. The cost reductions enterprises modeled assumed AI would slot cleanly into existing workflows and cut headcount. Real deployments are messier. The savings show up unevenly, the integration cost is higher than anyone budgeted, and the token-burn problem above means the running cost is higher too. The ROI isn't necessarily absent. It's just not arriving on the schedule the spreadsheets promised.

For builders, this is the counter-narrative you should keep close. The market is rewarding AI exposure right now, but the buyers writing checks are starting to ask harder questions about returns. If you're selling into enterprises, the era of "we're an AI company" as a complete pitch is closing. The next question is "what does it actually save me, measured, this quarter." Have that answer ready, with numbers, or your pipeline stalls when the euphoria cools.

GitLab cutting 14% of its workforce and exiting 22 countries in an AI-driven restructuring (Reuters via MSN) is what the cost-cutting looks like when a company decides not to wait for the returns to materialize organically. Whether that pays off or just trades long-term capability for short-term margin, we'll find out.

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Compaction: distill your agent's context before it hits 100% and cut tokens 84%

Given everything above about token burn, this is the most immediately useful thing you can do this week. Anthropic's engineering team documented compaction: when a context window nears its ceiling, you high-fidelity-summarize it so the agent continues with minimal performance loss instead of either truncating or paying for an enormous window. On a 100-round web search task, compaction dropped token consumption by about 84%. (Anthropic Engineering)

Eighty-four percent. On long-running tasks. That's not a tuning tweak, that's the difference between a viable product and one that loses money on every run.

The key detail that's easy to miss: trigger compaction before you hit 100% capacity, not after. If you wait until the window is full, you've already paid for all those tokens and you're forced into a lossy compression under pressure. Budget the compaction to fire earlier, while you still have room to do a clean summary. Pair it with structured note-taking so the agent reloads only distilled state rather than re-reading its entire history every turn.

I've been doing a crude version of this by hand on my own agent loops, dumping intermediate state to a notes file and clearing the working context. The 84% number tells me I've been leaving a lot on the table by not formalizing it. The principle generalizes beyond Claude: any long-horizon agent loop accumulates context that's mostly dead weight after a few rounds. The early decisions matter, the intermediate reasoning mostly doesn't, and you're paying full price to keep re-reading all of it.

This is the practical answer to the OpenRouter problem. Agents burn tokens because their context grows unbounded over a long task. Compaction caps that growth. If you build agents and you're not doing this, it's the highest-leverage cost change available to you right now, and it costs you an afternoon of implementation.

Section Deep Dives

Security

Exploit code is public for a one-click RCE in Flowise (CVE-2026-40933, CVSS 9.9). The Custom MCP stdio adapter in Flowise unsafely serializes commands, so an authenticated attacker can register an MCP stdio server with an arbitrary OS command. Obsidian Security showed that merely importing a crafted chatflow triggers server-side execution, and the PoC is now out. Affects flowise/flowise-components ≤3.0.13, patched in 3.1.0. Mitigation if you can't patch immediately: switch Custom MCP transport from stdio to SSE. (SecurityWeek) Flowise has 52K+ stars, so the install base is large. The real lesson is broader than one CVE. MCP stdio adapters are an under-appreciated RCE surface across the whole agent-builder category, and "import this workflow" is the new "open this attachment."

Anthropic mapped a full year of AI-enabled cyber threats onto MITRE ATT&CK. The analysis covers March 2025 through March 2026 and follows their earlier disclosure of the first reported AI-orchestrated cyber-espionage campaign. They argue cybersecurity has hit an inflection point because agentic models lower the barrier to multi-stage intrusions. (Anthropic) What's useful here for defenders is that you get a concrete threat taxonomy mapped to a framework your team already uses, not another abstract "AI is scary" warning. Pull it into your threat modeling.

University of Toronto demonstrated an AI worm that can target any online device. It's a proof-of-concept, but it autonomously propagates and adapts across internet-connected devices. (University of Toronto) Combined with the Anthropic threat report, the picture is that agentic self-propagation is moving from theory to demonstrated capability. The defensive implication: assume attackers get the same agent leverage you do.

A developer spent $1,500 finding out how well LLMs can actually hack an app. He built a deliberately vulnerable app and ran LLMs against it to measure real offensive capability, documenting it in a writeup that hit 285 points on HN. (kasra.blog) This is the kind of hands-on data point I trust more than vendor claims. If you're worried about automated exploitation, read the actual results instead of the headlines.

Agents

Claude Opus 4.8 shipped June 3 with a user-facing "effort" control. You can now dial how much compute the model spends per task, plus Claude Code improvements to MCP, terminal workflows, OTEL resource labels, and parallel tool handling. (Anthropic Release Notes) The effort dial is the interesting bit for builders. It's a direct cost/quality lever you can tune inside an agent loop, which pairs perfectly with the token-burn problem. Crank effort down for routine subagent work, up for the hard reasoning steps.

Claude Mythos Preview tops SWE-bench Verified at 93.9%. That's ahead of Opus 4.8 (88.6%) and Opus 4.7 Adaptive (87.6%) on 500 real GitHub-issue patches. (BenchLM) Crossing into the low-90s signals the benchmark is running out of headroom. More telling is the gap between a research "preview" model and shipping models, which suggests the next coding-agent step-change is already in the pipeline. I'd take any preview number with caution until it ships, but the trajectory is clear.

Coralogix raised $200M to build the monitoring layer for AI agents. The thesis is that production agents need dedicated observability to track behavior, troubleshoot failures, and provide oversight. (TechCrunch) Agent observability as a required enterprise category tracks with everything else this week. If agents burn unpredictable tokens and behave non-deterministically, you can't run them in production blind. This is infrastructure catching up to reality.

Research

Streaming communication cuts latency in multi-agent reasoning. Yang et al. attack the "generate-then-transfer" bottleneck where multi-agent latency scales linearly with pipeline depth, by streaming intermediate tokens between agents instead of waiting for full completions. (arXiv 2606.05158) If you run agent pipelines where serial hand-offs dominate wall-clock time, this decouples end-to-end latency from chain length. Directly applicable to anyone building deep agent chains.

Failed reasoning traces contain a fixability signal you can't get by re-reading them. Islah et al. challenge the reflex of throwing more compute at failed problems, arguing the trace tells you whether a failure is structurally recoverable, and that signal isn't extractable by just re-reading the chain-of-thought. (arXiv 2606.05145) Practical takeaway: blind retries waste budget on failures that can't be fixed by retrying. Smarter retry allocation is real money saved in an agent loop.

FoeGlass shows in-context learning is enough to red-team audio deepfake detectors. Dehdashtian et al. demonstrate that simple ICL suffices to probe and evade audio deepfake detection models without elaborate attack infrastructure. (arXiv 2606.05101) If you're deploying voice authentication or deepfake detection, treat it as defeatable by a motivated attacker with a laptop, not as a hard barrier.

AutoLab benchmarks frontier models on the full long-horizon research loop. Xu et al. test whether models can propose changes, run experiments, measure outcomes, and iterate, rather than one-shot tasks. (arXiv 2606.05080) A useful yardstick if you're evaluating autonomous agent harnesses against realistic iterative work instead of toy benchmarks.

Infrastructure & Architecture

VoidZero, the team behind Vite, Vitest, Rolldown, and Oxc, is joining Cloudflare. They've assured Vite stays open source and vendor-agnostic. (Cloudflare Blog) I have complicated feelings here. The JavaScript build toolchain that a huge fraction of the frontend ecosystem depends on is now under a single infrastructure company. The open-source assurances are nice, but consolidation of foundational tooling is worth watching with clear eyes. The incentives of an infra company and a neutral toolchain don't always align.

Oracle Cloud Infrastructure revenue surged 84% to $4.9B on AI workloads. Total Oracle cloud revenue was up 44% YoY to $8.9B in fiscal Q3 2026. (Foreign Policy Journal) The signal for builders is where the AI infrastructure budget is actually flowing. Enterprises are concentrating compute spend on providers that can land big training and inference contracts, which pressures mid-tier cloud and PaaS vendors.

Tools & Developer Experience

Microsoft open-sourced ASSERT, which spins up AI behavior tests from plain-text descriptions. Adaptive Spec-driven Scoring for Evaluation and Regression Testing lets developers write evals in natural language. (TechCrunch) Eval coverage is the unglamorous bottleneck for shipping agents safely. Anything that lowers the barrier to systematic evals is worth a look, and open-source means you can run it in your own pipeline. I'm going to test it against my synthesis agents.

Microsoft shipped portable policy files to control agent behavior. The spec lets developer, compliance, and security teams define agent policies in portable files that agents must follow. (TechCrunch) This is the governance plumbing enterprises need before they'll let agents into regulated workflows. Portable is the key word, since policy that travels with the agent across surfaces is more useful than policy locked to one platform.

upstash/context7 hit 56.7K stars serving version-accurate docs to LLMs via MCP. It addresses the stale-training-data problem that makes agents hallucinate outdated APIs. (GitHub) If you use Claude Code or Cursor and you're tired of the model confidently calling a function that was removed two versions ago, this is a practical MCP plug-in to ground generation in real docs. I've found it genuinely cuts the "that API doesn't exist anymore" frustration.

Models

MiniMax M3 claims frontier coding, 1M context, and native multimodality in open weights. It posts 59.0% on SWE-Bench Pro, 66.0% on Terminal-Bench 2.1, and 83.5 on BrowseComp, edging GPT-5.5 but below Claude Opus 4.8, at roughly 5–10% of frontier cost ($0.30/$1.20 per 1M on launch promo). (VentureBeat) The caveat is large: all figures are vendor-run on MiniMax's own infra, and the open weights hadn't shipped at launch, promised within ten days. Wait for the actual weight drop before trusting any of it. Vendor benchmarks on vendor hardware are marketing until proven otherwise.

OpenAI updated GPT-Rosalind for life sciences, using up to 31% fewer tokens. It combines GPT-5.5's agentic coding with stronger genomics and medicinal-chemistry intelligence, beats GPT-5.5 in every tested domain, and opened its research preview worldwide. (OpenAI) The 31% token reduction on long-horizon genomics analyses is the builder-relevant number, and it's the same theme as everything this week: capability per token is the metric that matters now. A paired Biodefense program sponsors vetted access for outbreak modeling.

Ideogram 4.0 and Reve 2.0 both shipped layout-based image generation June 3. Ideogram 4.0 is a 9.3B downloadable model with native 2K output and structured JSON prompting, topping open-weight rankings on DesignArena, though weights are non-commercial and need a paid production license. (Ideogram) Reve 2.0 ranked #2 on Arena text-to-image behind GPT Image 2. (Latent Space) JSON-layout control is the real shift for design-tool builders. Prompt-only is giving way to explicit layout specification as the competitive axis. As someone with a design background, this is the change I've wanted. Telling a model where things go beats hoping it guesses.

Vibe Coding

Unsloth Studio shipped a web UI at 65.7K stars for fine-tuning and running open models locally. It names Gemma 4, Qwen3.6, DeepSeek, and gpt-oss explicitly. (GitHub) The move from CLI/library to a hosted-local studio lowers the barrier for on-device fine-tuning considerably. Paired with the Gemma 4 release up top, a solo builder can now download a multimodal model and fine-tune it on a single GPU through a UI. That stack didn't exist in a usable form a year ago.

Gitlawb/openclaude hit 28.3K stars as a model-agnostic open coding-agent CLI. Marketed as "runs anywhere, uses anything," it joins the wave of open Claude Code-style alternatives. (GitHub) The high star count signals real demand for vendor-neutral terminal coding agents. Worth comparing against Nano-Collective/nanocoder (2K stars), which targets the same model-agnostic niche. I haven't moved off Claude Code myself, but the pull toward portability is strong enough that I'm tracking it.

SaaS Disruption

Stripe announced 288 products at Sessions 2026, including per-token streaming micropayments on the Tempo blockchain. The Agentic Commerce Suite includes a Link "agent wallet" that lets AI agents pay autonomously, and streaming payments combine Metronome usage tracking with stablecoin micropayments to bill per-token in real time. (Stripe) This solves a problem existing rails physically cannot: processing sub-cent sums every few milliseconds. It's infrastructure built for the agent-as-customer economy, not retrofitted SaaS billing. Connect it to the token-burn story and you see the whole picture forming. Agents consume per-token, so they'll pay per-token.

Seat-based pricing is collapsing across support, sales, and document review simultaneously. Vendors are moving to per-resolved-ticket, per-qualified-lead, and per-completed-review models. Gartner projects at least 40% of enterprise SaaS spend moves to usage-, agent-, or outcome-based pricing by 2030, with seat-based revenue share falling from 21% to 15%. (HighRadius) The pattern appearing across three unrelated categories at once marks it as structural, not vendor-specific. This is the OpenRouter token observation playing out in pricing pages.

OpenAI says enterprise is already 40% of revenue, tracking to parity with consumer by year-end. Coding agents represent roughly $3B and customer-support agents about $500M of that. (Yahoo Finance) This quantifies how fast agent-driven enterprise spend is displacing seat-based SaaS budgets, and validates coding and support as the two highest-revenue agent categories today. If you're picking a vertical to build an agent product in, the data says start where the money already is.

Vertical AI operating systems are raising Series A rounds across liquor retail, healthcare, and skilled trades. Scotch ($20M, liquor retail OS), Predoc ($30M, health-information management cutting record-retrieval time up to 75%), Rebar ($14M, HVAC/electrical/plumbing supply with computer-vision quoting that cuts quote time 60–70%). (Mean.ceo) The common architecture is one AI-native OS replacing a stack of legacy point tools in a regulated, workflow-heavy niche. Investors are funding domain depth and proprietary data over horizontal breadth. The new Series A bar is 10x value on day zero with time-to-value under 24 hours. (BuildMVPFast) For solo builders, the message is clear: pick a niche with hard workflows, own the data, skip the wrapper.

Policy & Governance

Trump signed a narrower AI executive order after industry pushback. The revised June 2 order requires only voluntary prerelease government reviews of advanced models, down from earlier mandatory-review drafts. (TechCrunch) The walk-back shows how much leverage the labs retain over federal oversight. "Voluntary" is doing all the work in that sentence.

AI industry rivals co-signed an open letter to Congress on bioweapon protections. Major competitors set aside rivalries to push for tougher safeguards against AI-aided biological weapons. (The Verge) When companies that fight over everything align on one risk, it's worth taking the risk seriously. This is the rare case where the industry is asking to be regulated.

Bernie Sanders introduced a bill giving the public a 50% stake in top AI companies. It frames AI's upside as a public asset rather than purely private wealth. (Yahoo Finance) Unlikely to pass as written, but it's a marker of how fast the "who captures the gains" debate is escalating. Pair it with the Stanford HAI data showing entry-level software employment for ages 22–25 down ~20% since 2024 (CBS News) and you see why this is becoming a political fight, not just an economic one.

Skills of the Day

1. Trigger context compaction before 100% capacity, not after. When your agent's window nears its ceiling, high-fidelity-summarize it and continue from distilled state. Anthropic measured an 84% token drop over a 100-round task. Set the trigger early so you compress cleanly instead of under pressure.

2. Return 1–2K-token summaries from subagents, not full transcripts. Give each subagent its own isolated context and have it hand back only a condensed summary to the orchestrator. This keeps the lead agent's window clean across long horizons. It's the architecture behind Claude Code's fork pattern. Use it whenever a subtask reads many files.

3. Shift agent-memory work from retrieval time to storage time. Organize, relate, and compress memories at creation, then retrieve via multi-signal fusion (semantic + keyword + entity + temporal recency). New 2026 architectures hit ~7,000 tokens per retrieval versus 25K–100K for full-context, with >91% recall. That's a 3–4x token saving for the same answer quality.

4. Filter MCP tool descriptions, not just user input. Tool poisoning hides malicious instructions inside a tool's description metadata, so input sanitization misses it entirely. Strip hidden-instruction tags, cap description length, and run semantic filtering for instruction-like patterns across tool metadata before it reaches the model. Treat every tool description as untrusted.

5. Scan MCP tool responses for instruction-like content before they re-enter context. Add a response filter that flags imperative sentences aimed at an AI, references to system messages, and "send this data" directives. This blocks indirect prompt injection that arrives through returned content rather than the prompt. Combine with per-tool rate limits.

6. Rate-limit MCP tools per-tool and per-caller, with limits tuned to agents not humans. A limit calibrated for human pace is no defense against an agent firing hundreds of calls per second. Start every MCP server at minimal read-only scope and elevate privilege incrementally only when a privileged operation is first attempted.

7. Use the Opus 4.8 effort dial as a cost lever inside agent loops. Crank effort down for routine subagent work and up for the hard reasoning steps. It's a direct cost/quality knob you can tune per task, and given that agents now out-burn humans on tokens, treating compute as a per-step decision instead of a constant is real savings.

8. Build and test worker agents in isolation before adding an orchestrator. Princeton NLP found a single agent matched or beat multi-agent systems on 64% of tasks given the same tools, and 40% of multi-agent pilots fail within six months from picking the wrong pattern. Validate each worker alone, then layer the orchestrator on top. Default to the supervisor pattern before reaching for swarm or debate.

9. Replace blind retries with fixability triage on failed reasoning traces. Don't reflexively throw more compute at a failed problem. The trace carries a signal about whether the failure is structurally recoverable. Allocate retry budget to recoverable failures and abandon the unrecoverable ones early instead of burning tokens on a loop that can't converge.

10. Cut GRPO RL fine-tuning VRAM 50–90% with Unsloth to train reasoning models on one mid-tier GPU. GRPO uses group-based reward comparison as an on-the-fly baseline, eliminating the separate memory-hungry value model. Unsloth gets gpt-oss-20b GRPO training into 15GB of VRAM (free on Colab) with 1.2–1.7x longer context and no slowdown. Reasoning fine-tunes are now in reach for solo builders.

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