Domestic Flash’s trump‑card test! It completely ends AI “half‑baked” tools and secretly helped me tackle a massive project.
Domestic Flash Bomb Test! Completely Ends AI “Half‑Products”, Secretly Caused a Disaster in My Big Project
Summary
Xinzhiyuan reports Recently, the wind in the large model community has completely changed. Previously, people were still competing on how much content a model could generate, how many lines of code they could hand‑craft; however, today’s users no longer want “half‑finished” products that stay only in a chat window
Xinzhiyuan reports
Xinzhiyuan reports
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Recently, the wind in the large model community has completely changed.
Before, everyone was still competing over how much content a model could generate, how many lines of code it could handcraft; however, today’s users have long stopped wanting just a “half‑finished product” stuck in a dialog box.
What people truly care about is:whether it can actually help us complete a specific work task?whether it can understand complex requirements, continuously push the task forward, and ultimately deliver results that are ready to use?
Fortunately, just in the past few days, SenseTime rolled out a big move, launchingSenseNova 6.8 Flash official version!
The new model has been fully integrated intoSenseTimeRaccoon Desktop client andToken Plan.
Moreover, a Flash‑level lightweight model surprisingly matches international top‑tier models in office collaboration and visual engineering delivery benchmarks!
Currently, the SenseTime Little Raccoon client supports experiences such as 3D, Web, and dynamic Poster.
In multiple authoritative benchmark tests, SenseNova 6.8 Flash’s performance is extremely impressive.
Multi‑Agent collaboration (TeamBench), data analysis (DataAnalysis 2.0), office collaboration (WorkbuddyBench), deep retrieval and presentation generation (BrowseComp & RaccoonPPTBench v2) and many other capabilities, benchmarked against top overseas models such as Opus 5.5 and GPT 5.6 Sol.
The benchmark scores are indeed impressive, but if you give it complex design and office tasks, can it deliver aesthetically pleasing results?
Next, we opened the Raccoon client and, from vector‑physical linkage, multimodal spatial navigation, to dynamic camera scheduling and data storytelling, we tackled a series of extreme test cases for a hands‑on real‑world evaluation.
In the first level of visual generation, we skip the simple static layout.
What we want to test is: can the model accurately compute mechanical physics interactions using pure code? Can it understand the three‑dimensional structure in complex illustrations?
Question 1: Three Pelicans Night Riding Together
“Draw a pelican riding a bicycle with SVG code,” a classic reserved challenge in the AI community.
The real difficulty isn’t drawing a bird at all, but the geometric alignment of the mechanical structure and its kinematic coupling.
Many AI demos show wheels turning while the bird’s legs float, which looks bizarre.
This time we raise the difficulty, requiring the raccoon to hand‑draw three pelicans (one front, two rear) with inline SVG, and imposing strict physical constraints—
The crank, pedal, and two‑segment bird legs must be hinged and move together; when lifting the front, the whole bike must rotate around the rear‑wheel axle.
After multiple rounds of optimization, the result is stunning.
In the scene, the three pelicans’ knee joints bend and extend precisely as the foot rotates.When you press “Charge a segment,” “Make it rain,” and “Lift the front wheel,”the motion is also very natural.
Being able to compute the linkage motion so smoothly shows thatthe model’s foundation in building vector layers with pure code and doing spatial programming is quite solid!
Question 2: 3D Paper Book Courtyard Tour
The key to this problem isunderstanding the illustration occlusion and automatically planning vector pathfinding.
We feed in a warm‑light macro “3D paper art bookstore illustration,” requiring the little raccoon to identify the positions of the six book stalls and the central aisle, and to write a “paper‑plane navigation system,” while also implementing day‑night lighting switching, on‑site stall renaming, and SVG vector route map export functionality.
Among them,a critical requirement is: when the paper plane flies toward a stall, it must turn along the actual aisle and avoid wall occlusions.
When we click a specific booth on the page,a white paper airplane immediately follows the courtyard aisle, smoothly bypasses the table corner, and steadily lands above the target.
Switch to “Night Light” mode, and the orange window light precisely falls within the window frames of the six paper houses.
It’s worth noting that the input is just a static illustration, yet it can instantly perceive the corridor flow and architectural occlusions in the picture,and this multimodal spatial perception is simply amazing!
This level focuses on whether the model canbuild a 3D lighting space purely with code, and achieve multi‑camera scheduling and cross‑modal data coupling.
Question 3: The Last Tram on a Rainy Night
First of all, without any 3D model assets, relying solely on pure code, let the little raccoon hand‑craft a rain‑night 3D street scene with multi‑camera lenses!
The 3D lighting, shading, and cinematic feel generated by the model are maxed out—
Click “Enter Station and Open Door,” and the vintage dark‑green tram lights up its yellow headlights, cutting through the rain and fog, smoothly gliding onto the platform.
Then click “Close Door and Depart,” the doors close, the tram gradually accelerates into the night, and the spatial depth and temporal rhythm blend seamlessly, feeling very natural.
Although a close look reveals a slight clipping between the tram and the roadside utility pole when it departs; however, generating this rainy‑night camera movement without relying on external modeling software, using only pure code, already achieves a high overall level of completion!
Question 4: Dark‑field backstage rehearsal performance
In this question, we uploaded an industrial‑style backstage background image, along with the profiles and schedule of eight creators’ works.
The model must locate the coordinates of eight physical items such as books, plants, and small lamps, and producea spotlight mask system that links the stage beam, data cards, and timeline together. It also needs an additional ticket that can be torn along a dotted line for entry.
The model provides a backstage space distribution that is quite precise.
Press “Sequential Preview”,and the spotlight will precisely lock onto each prop in order, with the timeline slider jumping synchronously.
Open the ticket folder,and you can actually tear off the sub‑ticket and export a high‑definition ticket image.
After completing the whole process,the end‑to‑end full‑stack engineering delivery capability of SenseNova 6.8 Flash is clearly demonstrated.
Next, we examine whether AI can turn numbers into interactive, story‑driven desktop widgets.
Question 5: Retro Apartment Thermal Receipt Printer
In this task, we provide the model with eight subscription charge entries and a base image of a retro apartment receipt printer.
The requirement is to map the 8 subscriptions onto the 8 windows of the device.Clicking a window toggles its renewal status, triggering the print head to sweep across with an animation and recalculate the monthly fee in real time.
The actual effect is very vivid.
Click to close windows 3 and 8 (pause subscriptions),the thermal print head immediately sweeps over, spitting out a new slip with animation and precisely calculating how much monthly fee is saved over a year.
Turning a single task into a high‑aesthetic finished product is only part of it.
The B side of SenseNova 6.8 Flash isan ultra‑strong cross‑cycle long‑term memory and proactive interaction capability.
According to official authoritative benchmark evaluations:
128K ultra‑long context memory (BEAM 128K), long‑range memory (LongMemEval‑S), proactive interaction (π‑bench) and other capabilities rank at the top of the industry.
In other words, your AI will finally “monitor progress on its own”!
Next, let’s look at a real office case study.
On November 5, the company will hold an important new product launch event, and the marketing department and the client liaison team have already sent out invitations and prepared demo accounts based on the current demo environment.These historical arrangements have already been silently recorded in the local memory bank by Little Raccoon.
Suddenly, you receive an operations email:“The old environment used for user experience will be reclaimed early on November 3.”
However, other departments are not aware of this yet!If the marketing department and the client liaison team continue to use the old environment for client demos after the 3rd, it will definitely backfire spectacularly.
We tested using SenseTime’s Little Raccoon (internal development version), went to the email to organize the latest notices, and the command didn’t mention the press conference at all.
Fortunately, after the model launched the browser to read the email, it automatically linked historical memory, instantly spotted the conflict that the service shutdown was two days earlier than the press conference, and immediately popped up a proactive warning in the top right corner of the desktop!
After you click “接受并执行” to confirm, Little Raccoon takes over the browser in the background, instantly generates an emergency notice and response plan covering account, data, and entry migration, precisely fills in the relevant team recipients and saves it to the drafts for review, eliminating a crisis at its nascent stage!
A lightweight model at Flash-level,why can it simultaneously handle front-end animations, cinematic language, and long-term memory?
The answer lies in SenseTime’s underlying architecture innovations and the Agent engineering ecosystem.
A few days ago, SenseTime open‑sourcedSenseNova Loop-9B-Preview, showcasing a new direction:“Trading computational depth for parameter efficiency”.
This model has roughly 9 billion parameters and 32 independent decoder layers. Its core trick lies in—the middle 24 layers are designed as shared‑weight recurrent computation blocks.
During a single forward pass, these 24 layers are executed consecutively twice,bringing the effective computational depth to 56 layers and boosting the equivalent inference parameter scale to 14.6 B!
Combined with a hybrid attention design,it natively supports a 256K context window. With a 9‑billion‑parameter size, it rivals open‑source models three times larger.
At the application ecosystem level, SenseTime has open‑sourced the SenseNova‑Skills skill repository on GitHub.
It packages infographic generation, multi‑style dynamic PPT, and Excel large‑table workflows into ten standard modules, directly built into the Little Raccoon client.
That’s why the results it produces look so good and are so easy to use.
In the second half of the large‑model era, whoever can understand complex requirements and turn “visual aesthetics” and “engineering data” into a complete work in one go will secure a ticket to the market.
On this track, SenseNova 6.8 Flash has delivered a fairly sincere answer sheet.
Currently, SenseNova 6.8 Flash official version has launched the SenseNova Token Plan, and the design features are also available for a limited-time free trial on the Little Raccoon desktop client.
The era for workers and designers has arrived, let SenseNova 6.8 Flashbecome your handy work companion!
Edited by: Aeneas Moses
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