I found after some research that the most numerically stable way to solve the least squares problem is through QR factorization. For $n$ number of observations and $p$ number of parameters it takes the following form:
$$Q_{n,p}R_{p,p} = X_{n, p} $$
Where $R$ is a square upper-triangular and $Q$ is orthogonal. And the solution is:
$$ \mathbf{\hat{\beta}} = \left(\mathbf{X}^T \mathbf{X}\right)^{-1} \mathbf{X}^T z $$ $$ \mathbf{\hat{\beta}} = R^{-1} Q^T z $$
I am interested on continuouly update my least squares model $\hat{\beta}$ as new data arrives $x_{n+1},x_{n+2},x_{n+3}... \in R^{p}$, we can define $X^{*} = \begin{bmatrix} X \\ x_{n+1} \\ x_{n+2} \\ ... \end{bmatrix}$ with added rows and items on $y$.
I found that is somehow called rank k matrix update or related to symmetric rank k update (SYRK) level 3 BLAS. I could not find more information on how formulate this on my LS QR factorization.
Any ideas or hints are really welcome.