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Abacus.AI’s Smaug Agentic fine-tune targets the failure tail in long-running coding agents

The useful part of Smaug Agentic is not another frontier-style benchmark claim. Abacus.AI is publishing a drop-in Kimi K3 derivative that targets a specific production failure mode in coding agents: long runs that burn the reasoning budget without converging. The weights and model card are public, but the training data is not disclosed and the benchmark gains remain vendor-produced.

Cloud Run adds singleton instances for long-lived, individually addressable workloads

Cloud Run instances sit between autoscaling serverless services and a small VM. They run one individually addressable container continuously, can be stopped and restarted, and use shared CPU economics; Google’s launch example prices 1 vCPU plus 1 GiB running for 30 days at $5.70.

SaaSPricing & BillingActive dossier

Paddle brings recurring Pix payments to SaaS subscriptions in Brazil

Brazilian customers can authorize Pix Automático mandates for Paddle subscriptions without a separate early-access application. The path broadens local-payment access for SaaS, while delayed renewals, fixed mandate amounts and re-authorisation requirements still create implementation caveats.

Claude Code Projects turns one engineering goal into parallel cloud-agent branches

The important change is not simply that Claude can run several agents. Projects now owns decomposition, shared context, branch isolation and progress coordination across full Claude Code sessions, while the trade-offs become usage burn, cloud-only execution and ordinary merge conflicts when parallel work overlaps.

Jev becomes Vercel AI Gateway’s fastest-adopted model in its first 24 hours

Jev’s launch claims were interesting; Vercel’s usage data is more useful. Nearly 13% of paid AI Gateway teams tried the typed decision model in its first day, while Jev also rose to a material share of gateway requests. That does not establish retention or production success, but it is unusually fast developer uptake for a model designed to make bounded software decisions rather than generate prose.

SaaSProduct & GrowthActive dossier

Produktly’s 464-product dataset puts median SaaS tour completion at 29%

The strongest signal in Produktly’s 2026 onboarding dataset is not a universal target but a set of usable baselines: median tour completion was 29%, 1–2-step tours completed far more often than 9+ step tours, in-app NPS response rates were low, and announcement attention was heavily front-loaded. The report explicitly discloses sample and causal limitations.