Official product page from **DeepSeek**, published on **August 13, 2026**, **unsigned**, ~450 words, announcing the *developer preview* release of **DeepSeek Harness** (`dsh`) — a coding-agent harness **open source under the MIT license**, whose repository opened the same day. A three-word thesis, repeated in the title and in the repository description: *« Everything is a plugin »*, paired with a second promise, *« Every run is traceable »*. The page states the equation *« AGENT = MODEL + HARNESS »* and lists the pluggable capabilities — *« models, tools, skills, sessions, sandboxes, storage, loops, scheduling, and the UI »*. Four modes ship: **Standard** (full coding agent), **Code** (tools exposed via the *Code Mode SDK*, letting the model compose multi-step operations inside a TypeScript program), **Minimal** (*« two-tool coding agent with persistent bash and str_replace_editor »*, explicitly *« for benchmarking models in a minimal environment »*), and **Creator** (runtime inspection, in-memory plugin testing). The technical substance sits in the repository, not on the page: `docs/architecture.md` states a logging invariant — *« Model-visible means logged. Anything that reaches a model request must be reconstructable from the log, and a runtime invariant asserts it »* — and states that *« there is no privileged core to patch »*. The technical core is not DeepSeek's own: DSH is built on **Cordis** (the `cordiverse` project, a third party), **vendored** into `vendor/` with a manifest and a sync procedure, and the page places the *« Cordis paper »* at the same navigation level as "GitHub" and "Developer docs". Two LLM adapters ship — `dsh-llm-deepseek` and `dsh-llm-pi-ai`, a generic multi-provider adapter. The repository warns in capitals: *« THERE WILL BE COMPATIBILITY-BREAKING CHANGES »*, and `CLAUDE.md` specifies that `SESSION_FORMAT_VERSION` stays at `0` *« with no compatibility promise »*, with backends rejecting old on-disk formats. Timeline: DSH ships on the day **DeepSeek-V4-Pro reaches GA**, three days before a new API pricing schedule takes effect on **August 16, 2026 at 16:00 UTC**, with peak/off-peak rates and an off-peak discount of **−50%**.
#DeepSeek Harness#dsh#agent harness
**DeepSeek** (DeepSeek AI, laboratoire chinois) · en tant qu'institution. Page produit **non signée** : aucun auteur · aucun ingénieur mis en avant · aucun billet de blog ni papier technique associé. Le « nous » n'apparaît qu'une fois · en dernière phrase — *« We look forward to exploring the limits of intelligence with developers worldwide »*. Publiée le **13 août 2026**. La page est rendue en JavaScript : `curl` sur l'URL renvoie **HTTP 202 avec un corps vide** · le texte n'existant qu'après exécution du bundle. Deux documents de politique sont liés en pied de page — *Safe Use Policy* et *Data Processing Statement*.
SFEIR analysis (consulting-firm voice, "an engineer's reading") articulating two frameworks too often conflated: the **SDLC** (Software Development Life Cycle — *building the software correctly and reliably*) and the **PDLC** (Product Development Life Cycle — *building the right product and succeeding in the market*). Central thesis: the two cycles are not competitors but **nested** — the SDLC is the subset of the PDLC **housed under its development phase**; when a product team reaches the "build" stage, a full SDLC cycle (design → build → test → review → deployment) runs inside it. The SDLC is standardized (**ISO/IEC/IEEE 12207**, 2017 and 2026 editions), with its lineage of models (Waterfall 1970, V-model, iterative/spiral, **Agile 2001**, **DevOps/DevSecOps 2009+**) and its **DORA** metrics (throughput, stability, MTTR, change failure rate). The PDLC, being the umbrella cycle, runs from **ideation/discovery** to **market withdrawal** (not to be confused with the marketing **PLC** of Theodore Levitt, 1965, which describes a *commercial curve*, not *organized work*: "the PLC observes a curve; the PDLC organizes work"). **Tipping point**: the SDLC natively addresses **only one risk in four** — via **Marty Cagan's "Four Big Risks"** framework (Value → PM, Usability → Designer, Feasibility → Lead Engineer, Business viability → PM) — an organization excellent at SDLC but blind to PDLC produces "software nobody wants" — John Cutler's **"feature factory"** (success measured by output, not outcome). **Why AI changes everything**: generative AI **compresses the SDLC** (Google/JetBrains data, May 2026: **~85% of developers** regularly use coding agents, **~41% of new code** is AI-generated; implementation goes from weeks to hours), so the **bottleneck shifts upstream** — deciding *what* to build (Marty Cagan, April 2026: "when the cost of delivery collapses, the bottleneck shifts to discovery"). Consequences: DORA 2025 (~5,000 professionals, 90% AI adoption) shows a **positive correlation with throughput but a negative one with stability** (more unvalidated features means instability and rework); Andrew Ng (AI Startup School, July 2025) reports teams **reversing the "1 PM for 4 engineers" ratio to "2 PMs for 1 engineer"**; and with **spec-driven development**, the PDLC/SDLC boundary becomes **porous** (the product spec becomes directly executable by agents). **What a CIO should take away**: an augmented SDLC becomes a **market standard, not a differentiator** — the junction with the product must be instrumented, **executable specifications** demanded as input, technical metrics cross-referenced with outcome metrics, and the role of "feature supplier" **refused**. For a CPO: the shift of the bottleneck toward discovery is both a **promotion** (product judgment becomes scarce again) and a **notice to act** (industrialize discovery to reach parity with the SDLC). SFEIR's in-house framework ("Designing and building in the agentic era" — **11-phase cycle** + **Software Factory 10x**) is positioned as the answer on the engineering side, with the **articulation of the two cycles** as the next lever. Conclusion: "as code becomes a commodity, margin shifts toward product judgment and governance."