我的征尘是星辰大海。。。

The dirt and dust from my pilgrimage forms oceans of stars...

-------当记忆的篇章变得零碎，当追忆的图片变得模糊，我们只能求助于数字存储的永恒的回忆

作者:黄教授

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关于DeepSeek-Harness的开源的策略讨论

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Technical Essay: In-depth insights on Deepseek Harness's current open-source strategy.
Overall, the underlying necessity for Deepseek to launch Harness is beyond doubt.
From model iteration, API usage ecosystem, capability solidification, unification of interaction paradigms, to building a closed-loop training data feedback chain, at the current stage of large model development, a self-developed dedicated front-end scheduling scaffolding is already a must-have at the strategic level.
Without a unified official Harness, models will always have to passively adapt to a wide variety of third-party Agent frameworks.
Chaotic invocation paradigms, fragmented data trajectories, and unattributable failed cases will inevitably limit the precise convergence and advanced iteration of model capabilities in the long run.
From the perspective of launch rhythm and open-source strategy alone, Deepseek's current choice is essentially a phased lazy iteration amid time constraints.
The team itself needs industry buzz, a quick foothold in the Agent track, and needs to seize discourse power over industry paradigms.
Meanwhile, the team has not yet polished a finalized optimal workflow.
In the absence of a mature standard answer, the official team did not choose to close development internally and release the product only after polishing it into a final finished version.
Instead, it open-sourced the highly flexible, freely combinable, trial-and-error friendly original framework in advance.
Its underlying logic is very clear: there is no absolutely unique standard answer for the optimal tool combination and scheduling process context paradigm of Coding Agent.
This work has a very strong attribute of engineering tuning. Just like a cooking recipe, the basic logic is fixed.
But the final optimal combination, parameter adaptation, and process trade-offs can only be precipitated through a large number of permutations, combinations and trial-and-error iterations.
Precisely because there is no confirmed optimal solution, Deepseek chose a two-pronged approach.
Internally, it takes this original framework as an experimental platform for rapid iteration, aggressive trial-and-error, and screening of the optimal paradigm.
Meanwhile, it opens the framework to the open-source community, allowing global developers to jointly participate in plug-in combination, process transformation and scenario verification, expand the trial-and-error boundary with the power of the community, and complete large-scale exploratory experiments at low cost.
Essentially, Harness at the current stage is not a finished tool delivered to end users, but an open public experimental bench.
But objectively speaking, we should not place too high expectations on the convergence capability of the open-source community.
The community is inherently good at divergent innovation, polishing niche scenarios, and developing various plug-in prototypes.
But it will not, nor has the incentive to, complete the core work of standardization, engineering and optimal solution convergence for the official team.
Paradigm unification, process solidification, in-depth two-way adaptation between models and scaffolding, and polishing of industrial-grade optimal solutions can ultimately only be completed by Deepseek's official team with core resources invested.
The community can only serve as an incremental supplement for auxiliary trial-and-error, and can never replace the official core R&D and convergence work.
If the official team does not invest sufficient resources, cannot keep up with the required iteration intensity, and only relies on the spontaneous progress of the open-source community, it will be very difficult for this seemingly promising architecture to eventually precipitate a standard paradigm that can truly unify the industry's closed-loop iteration.